Pietro Lio | Cambridge Public Health

Research includes predictive models in Biomedicine;
multiscale modelling of molecules-cell-tissue-organ interactions;
development and testing of methodologies for modelling biomedical systems
www.cl.cam.ac.uk/~pl219

Publications from Elements

Journal articles

2025

Prinzi, F., Barbiero, P., Greco, C., Amorese, T., Cordasco, G., Liò, P., Vitabile, S. and Esposito, A., 2025. Using AI explainable models and handwriting/drawing tasks for psychological well-being Information Systems, v. 127
Doi: http://doi.org/10.1016/j.is.2024.102465

2024 (Published online)

Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lio, P., Lepri, B. and Passerini, A., 2024 (Published online). Explaining the Explainers in Graph Neural Networks: a Comparative Study ACM Computing Surveys,
Doi: 10.1145/3696444

Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Lió, P., 2024 (Published online). Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting Nature Communications, v. 15
Doi: 10.1038/s41467-024-45566-8

2024 (Accepted for publication)

Thaventhiran, J., 2024 (Accepted for publication). Intratumoral antigen signaling traps CD8+ T cells to confine exhaustion to the tumor site Science Immunology,
Doi: 10.1126/sciimmunol.ade2094

Buterez, D., Janet, JP., Kiddle, S., Oglic, D. and Lio, P., 2024 (Accepted for publication). Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting Nature Communications,

2024

Anand, R., Joshi, CK., Morehead, A., Jamasb, AR., Harris, C., Mathis, SV., Didi, K., Hooi, B. and Liò, P., 2024. RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. ArXiv,

Bazaga, A., Liò, P. and Micklem, G., 2024. UNSUPERVISED PRETRAINING FOR FACT VERIFICATION BY LANGUAGE MODEL DISTILLATION 12th International Conference on Learning Representations, ICLR 2024,

Oliva, V., Martone, A., Fanelli, G., Domschke, K., Minelli, A., Gennarelli, M., Martini, P., Bortolomasi, M., Maron, E., Squassina, A., Pisanu, C., Kasper, S., Zohar, J., Souery, D., Montgomery, S., Albani, D., Forloni, G., Ferentinos, P., Rujescu, D., Mendlewicz, J., De Ronchi, D., Baune, BT., Potier, MC., van Westrhenen, R., Rybakowski, F., Mehta, D., Dierssen, M., Janzing, JGE., Liò, P., Serretti, A. and Fabbri, C., 2024. Polygenic scores of subcortical brain volumes as possible modulators of treatment response in depression Neuroscience Applied, v. 3
Doi: 10.1016/j.nsa.2024.103937

Bi, X., Yang, Z., Liu, B., Cun, X., Pun, C-M., Lio, P. and Xiao, B., 2024. ZeroPur: Succinct Training-Free Adversarial Purification. CoRR, v. abs/2406.03143

Cao, P. and Lio, P., 2024. GenRec: Generative Personalized Sequential Recommendation. CoRR, v. abs/2407.21191

Siebenmorgen, T., Menezes, F., Benassou, S., Merdivan, E., Didi, K., Mourão, ASD., Kitel, R., Liò, P., Kesselheim, S., Piraud, M., Theis, FJ., Sattler, M. and Popowicz, GM., 2024. MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery. Nat Comput Sci, v. 4
Doi: http://doi.org/10.1038/s43588-024-00627-2

Luca, VMD., Longa, A., Passerini, A. and Liò, P., 2024. xAI-Drop: Don't Use What You Cannot Explain. CoRR, v. abs/2407.20067

Liu, L., Cheng, Y., Deng, Z., Wang, S., Chen, D., Hu, X., Liò, P., Schönlieb, CB. and Aviles-Rivero, A., 2024. TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios MM 2024 - Proceedings of the 32nd ACM International Conference on Multimedia,
Doi: 10.1145/3664647.3681153

Shen, X., Lio, P., Yang, L., Yuan, R., Zhang, Y. and Peng, C., 2024. Graph Rewiring and Preprocessing for Graph Neural Networks Based on Effective Resistance IEEE Transactions on Knowledge and Data Engineering, v. 36
Doi: 10.1109/TKDE.2024.3397692

Gantz, M., Mathis, SV., Nintzel, FEH., Lio, P. and Hollfelder, F., 2024. On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering. Faraday Discuss, v. 252
Doi: 10.1039/d4fd00065j

Lawrence, E., El-Shazly, A., Seal, S., Joshi, CK., Liò, P., Singh, S., Bender, A., Sormanni, P. and Greenig, M., 2024. Understanding Biology in the Age of Artificial Intelligence. CoRR, v. abs/2403.04106

Yang, Z., Zhang, G., Wu, J., Yang, J., Sheng, QZ., Xue, S., Zhou, C., Aggarwal, C., Peng, H., Hu, W., Hancock, E. and Liò, P., 2024. State of the Art and Potentialities of Graph-level Learning ACM Computing Surveys, v. 57
Doi: 10.1145/3695863

Mamalakis, M., Mamalakis, A., Agartz, I., Mørch-Johnsen, LE., Murray, GK., Suckling, J. and Lio, P., 2024. Solving the enigma: Deriving optimal explanations of deep networks. CoRR, v. abs/2405.10008

Bardozzo, F., Terlizzi, A., Simoncini, C., Lió, P. and Tagliaferri, R., 2024. Elegans-AI: How the connectome of a living organism could model artificial neural networks Neurocomputing, v. 584
Doi: http://doi.org/10.1016/j.neucom.2024.127598

Du, Y., Jamasb, AR., Guo, J., Fu, T., Harris, C., Wang, Y., Duan, C., Liò, P., Schwaller, P. and Blundell, TL., 2024. Machine learning-aided generative molecular design Nature Machine Intelligence, v. 6
Doi: http://doi.org/10.1038/s42256-024-00843-5

Ali, R., Kulyte, P., Borde, HSDO. and Liò, P., 2024. Metric Learning for Clifford Group Equivariant Neural Networks. CoRR, v. abs/2407.09926

Yang, L., Liò, P., Shen, X., Zhang, Y. and Peng, C., 2024. Adaptive multi-scale Graph Neural Architecture Search framework Neurocomputing, v. 599
Doi: http://doi.org/10.1016/j.neucom.2024.128094

Mumenin, N., Yousuf, MA., Nashiry, MA., Azad, AKM., Alyami, SA., Lio', P. and Moni, MA., 2024. ASDNet: A robust involution-based architecture for diagnosis of autism spectrum disorder utilising eye-tracking technology IET Computer Vision, v. 18
Doi: 10.1049/cvi2.12271

Li, M., Micheli, A., Wang, YG., Pan, S., Lio, P., Gnecco, GS. and Sanguineti, M., 2024. Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications IEEE Transactions on Neural Networks and Learning Systems, v. 35
Doi: 10.1109/TNNLS.2024.3371592

Wang, Z., Ma, J., Gao, Q., Bain, C., Imoto, S., Liò, P., Cai, H., Chen, H. and Song, J., 2024. Dual-stream multi-dependency graph neural network enables precise cancer survival analysis. Med Image Anal, v. 97
Doi: 10.1016/j.media.2024.103252

Caralt, FH., Gil, GB., Duta, I., Liò, P. and Alarcón-Cot, E., 2024. Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks. CoRR, v. abs/2407.20597

Yan, C., Lu, X., Lio, P., Hui, P. and He, D., 2024. Self-simulation and Meta-Model Aggregation Based Heterogeneous Graph Coupled Federated Learning IEEE Internet of Things Journal,
Doi: http://doi.org/10.1109/JIOT.2024.3462724

Georgiev, D., Liò, P. and Buffelli, D., 2024. The Deep Equilibrium Algorithmic Reasoner. CoRR, v. abs/2402.06445

Somathilaka, S., Ratwatte, A., Balasubramaniam, S., Vuran, MC., Srisa-an, W. and Liò, P., 2024. Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity. CoRR, v. abs/2403.08549

Kulyte, P., Vargas, F., Mathis, SV., Wang, YG., Hernández-Lobato, JM. and Liò, P., 2024. Improving Antibody Design with Force-Guided Sampling in Diffusion Models. CoRR, v. abs/2406.05832

Dong, T., Jamnik, M. and Liò, P., 2024. Sphere Neural-Networks for Rational Reasoning. CoRR, v. abs/2403.15297

Raisa, RA., Rodela, AS., Yousuf, MA., Azad, A., Alyami, SA., Lio, P., Islam, MZ., Pogrebna, G. and Moni, MA., 2024. Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study IEEE Access, v. 12
Doi: 10.1109/ACCESS.2024.3426928

Huang, K., Wang, YG., Li, M. and Liò, P., 2024. How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing Proceedings of Machine Learning Research, v. 235

Li, K., Zheng, J., Ni, W., Huang, H., Lio, P., Dressler, F. and Akan, OB., 2024. Biasing Federated Learning With a New Adversarial Graph Attention Network IEEE Transactions on Mobile Computing,
Doi: http://doi.org/10.1109/TMC.2024.3499371

Buterez, D., Janet, JP., Oglic, D. and Lio, P., 2024. Masked Attention is All You Need for Graphs. CoRR, v. abs/2402.10793

Defilippo, A., Veltri, P., Lió, P. and Guzzi, PH., 2024. Leveraging graph neural networks for supporting automatic triage of patients. Sci Rep, v. 14
Doi: 10.1038/s41598-024-63376-2

Lope, EGD., Deshpande, S., Torné, RV., Liò, P., Glaab, E. and Bordas, SPA., 2024. Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease. CoRR, v. abs/2406.14442

Sabari, A., Hasan, I., Alyami, SA., Liò, P., Ali, MS., Moni, MA. and Azad, AKM., 2024. LandSin: A differential ML and google API-enabled web server for real-time land insights and beyond[Formula presented] Software Impacts, v. 22
Doi: http://doi.org/10.1016/j.simpa.2024.100718

Bazaga, A., Liò, P. and Micklem, G., 2024. HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs. CoRR, v. abs/2402.07309

Zhao, X., Li, Z., Shen, M., Stan, G-B., Liò, P. and Zhao, Y., 2024. Enhancing Real-World Complex Network Representations with Hyperedge Augmentation. CoRR, v. abs/2402.13033

Jamasb, AR., Morehead, A., Joshi, CK., Zhang, Z., Didi, K., Mathis, S., Harris, C., Tang, J., Cheng, J., Liò, P. and Blundell, TL., 2024. Evaluating Representation Learning on the Protein Structure Universe. ArXiv,

Rowbottom, J., Maierhofer, G., Deveney, T., Schratz, K., Liò, P., Schönlieb, C-B. and Budd, CJ., 2024. G-Adaptive mesh refinement - leveraging graph neural networks and differentiable finite element solvers. CoRR, v. abs/2407.04516

Zhu, M., Bazaga, A. and Liò, P., 2024. FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models. CoRR, v. abs/2406.04501

Lu, X., Zhao, J., Zhu, S. and Lio, P., 2024. SNDGCN: Robust Android malware detection based on subgraph network and denoising GCN network Expert Systems with Applications, v. 250
Doi: http://doi.org/10.1016/j.eswa.2024.123922

Zhou, B., Zheng, L., Wu, B., Yi, K., Zhong, B., Tan, Y., Liu, Q., Liò, P. and Hong, L., 2024. A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity. Cell Discov, v. 10
Doi: http://doi.org/10.1038/s41421-024-00728-2

2023 (Published online)

Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2023 (Published online). Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data Proceedings of the AAAI Conference on Artificial Intelligence, v. 37
Doi: http://doi.org/10.1609/aaai.v37i8.26090

2023 (Accepted for publication)

Dittmer, S., Roberts, M., Gilbey, J., biguri, A., preller, J., Rudd, J., Aston, J. and Schönlieb, C-B., 2023 (Accepted for publication). Navigating the development challenges in creating complex data systems Nature Machine Intelligence,

Lu, M., Christensen, C., Weber, J., Konno, T., Laubli, N., Scherer, K., Avezov, E., Lio, P., Lapkin, A., Kaminski Schierle, G. and Kaminski, C., 2023 (Accepted for publication). ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology Nature Methods,
Doi: http://doi.org/10.1038/s41592-023-01815-0

Breger, A., Selby, I., Roberts, M., Babar, J., Gkrania-Klotsas, E., Preller, J., Escudero Sanchez, L., Rudd, J., Aston, J., Weir-McCall, J., Sala, E. and Schoenlieb, C., 2023 (Accepted for publication). A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data Scientific data,
Doi: 10.1038/s41597-023-02340-7

Buterez, D., Janet, JP., Kiddle, S., Oglic, D. and Lio, P., 2023 (Accepted for publication). Modelling local and general quantum mechanical properties with attention-based pooling Communications Chemistry,
Doi: http://doi.org/10.1038/s42004-023-01045-7

Rittman, T., Azevedo, T., Bethlehem, R., Whiteside, D., Swaddiwudhipong, N., Rowe, J. and Lio, P., 2023 (Accepted for publication). Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank Communications Medicine,
Doi: http://doi.org/10.1038/s43856-023-00313-w

Roberts, M., Rudd, J., Aston, J., Schoenlieb, C-B., Gilbey, J., preller, J. and Dittmer, S., 2023 (Accepted for publication). The Impact of Imputation Quality on Machine Learning Classifier Performance for Datasets with Missing Values Communications Medicine,

2023

Purificato, A., Cassarà, G., Liò, P. and Silvestri, F., 2023. Sheaf Neural Networks for Graph-based Recommender Systems. CoRR, v. abs/2304.09097

Jain, R., Velickovic, P. and Liò, P., 2023. Neural Priority Queues for Graph Neural Networks. CoRR, v. abs/2307.09660

Longa, A., Lachi, V., Santin, G., Bianchini, M., Lepri, B., Liò, P., Scarselli, F. and Passerini, A., 2023. Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities. CoRR, v. abs/2302.01018

Lu, Y., Borde, HSDO. and Liò, P., 2023. AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference. CoRR, v. abs/2311.11891

Ceccarelli, F., Giusti, L., Holden, SB. and Liò, P., 2023. Neural Embeddings for Protein Graphs. CoRR, v. abs/2306.04667

Yi, K., Zhou, B., Shen, Y., Liò, P. and Wang, YG., 2023. Graph Denoising Diffusion for Inverse Protein Folding Advances in Neural Information Processing Systems, v. 36

Viñas, R., Joshi, CK., Georgiev, D., Lin, P., Dumitrascu, B., Gamazon, ER. and Liò, P., 2023. Hypergraph factorization for multi-tissue gene expression imputation. Nat Mach Intell, v. 5
Doi: 10.1038/s42256-023-00684-8

Zhu, J., Yang, G. and Liò, P., 2023. A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning. CoRR, v. abs/2302.11184

Yang, J. and Liò, P., 2023. Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction. CoRR, v. abs/2312.00677

Georgiev, D., Numeroso, D., Bacciu, D. and Liò, P., 2023. Neural Algorithmic Reasoning for Combinatorial Optimisation Proceedings of Machine Learning Research, v. 231

Kazhdan, D., Dimanov, B., Magister, LC., Barbiero, P., Jamnik, M. and Liò, P., 2023. GCI: A (G)raph (C)oncept (I)nterpretation Framework. CoRR, v. abs/2302.04899

Duval, A., Mathis, SV., Joshi, CK., Schmidt, V., Miret, S., Malliaros, FD., Cohen, T., Lio, P., Bengio, Y. and Bronstein, MM., 2023. A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. CoRR, v. abs/2312.07511

Azzolin, S., Longa, A., Barbiero, P., Liò, P. and Passerini, A., 2023. GLOBAL EXPLAINABILITY OF GNNS VIA LOGIC COMBINATION OF LEARNED CONCEPTS 11th International Conference on Learning Representations, ICLR 2023,

Shen, Z., Cheng, Y., Chan, RH., Liò, P., Schönlieb, C-B. and Avilés-Rivero, AI., 2023. TRIDENT: The Nonlinear Trilogy for Implicit Neural Representations. CoRR, v. abs/2311.13610

Islam, MS., Hasan, KF., Sultana, S., Uddin, S., Lio', P., Quinn, JMW. and Moni, MA., 2023. HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN. Neural Netw, v. 162
Doi: 10.1016/j.neunet.2023.03.004

Joshi, CK., Jamasb, AR., Viñas, R., Harris, C., Mathis, SV. and Liò, P., 2023. Multi-State RNA Design with Geometric Multi-Graph Neural Networks. CoRR, v. abs/2305.14749

Crisostomi, D., Cannistraci, I., Moschella, L., Barbiero, P., Ciccone, M., Liò, P. and Rodolà, E., 2023. From Charts to Atlas: Merging Latent Spaces into One. CoRR, v. abs/2311.06547

Didi, K., Vargas, F., Mathis, SV., Dutordoir, V., Mathieu, E., Komorowska, UJ. and Lio, P., 2023. A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. CoRR, v. abs/2312.09236

Dimitri, GM., Spasov, SE., Duggento, A., Passamonti, L., Liò, P. and Toschi, N., 2023. Multimodal and multicontrast image fusion via deep generative models. CoRR, v. abs/2303.15963

Brant, I., Norcliffe, A. and Liò, P., 2023. Fourier Neural Differential Equations for learning Quantum Field Theories. CoRR, v. abs/2311.17250

Mei, X., Yang, Y., Li, M., Huang, C., Zhang, K. and Lió, P., 2023. A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution. CoRR, v. abs/2306.01500

Liu, L., Cheng, Y., Chen, D., He, J., Liò, P., Schönlieb, C-B. and Avilés-Rivero, AI., 2023. Traffic Video Object Detection using Motion Prior. CoRR, v. abs/2311.10092

Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Liò, P., Maggini, M. and Melacci, S., 2023. Logic Explained Networks. Artif. Intell., v. 314

Jürß, J., Magister, LC., Barbiero, P., Liò, P. and Simidjievski, N., 2023. Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts. CoRR, v. abs/2311.15112

Bernárdez, G., Telyatnikov, L., Alarcón, E., Cabellos-Aparicio, A., Barlet-Ros, P. and Liò, P., 2023. Topological Graph Signal Compression. CoRR, v. abs/2308.11068

Del Duca, S., Semenzato, G., Esposito, A., Liò, P. and Fani, R., 2023. The Operon as a Conundrum of Gene Dynamics and Biochemical Constraints: What We Have Learned from Histidine Biosynthesis. Genes (Basel), v. 14
Doi: http://doi.org/10.3390/genes14040949

Yeh, KF., Flood, PDL., Redman, W. and Liò, P., 2023. Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passing. CoRR, v. abs/2305.09060

Bardozzo, F., Terlizzi, A., Liò, P. and Tagliaferri, R., 2023. ElegansNet: a brief scientific report and initial experiments. CoRR, v. abs/2304.13538

Campbell, A., Zippo, AG., Passamonti, L., Toschi, N. and Liò, P., 2023. DBGSL: Dynamic Brain Graph Structure Learning Proceedings of Machine Learning Research, v. 227

Bongini, P., Scarselli, F., Bianchini, M., Dimitri, GM., Pancino, N. and Lio, P., 2023. Modular Multi-Source Prediction of Drug Side-Effects With DruGNN. IEEE/ACM Trans Comput Biol Bioinform, v. 20
Doi: 10.1109/TCBB.2022.3175362

Zhao, X., Stärk, H., Beaini, D., Liò, P. and Zhao, Y., 2023. Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration. CoRR, v. abs/2301.11517

Buterez, D., Janet, JP., Kiddle, SJ. and Liò, P., 2023. MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning. J Chem Inf Model, v. 63
Doi: http://doi.org/10.1021/acs.jcim.2c01569

Bazaga, A., Liò, P. and Micklem, G., 2023. Language Model Knowledge Distillation for Efficient Question Answering in Spanish. CoRR, v. abs/2312.04193

Yang, J. and Liò, P., 2023. Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network. CoRR, v. abs/2312.00661

Chowdhury, AA., Hasan Mahmud, SM., Shahjalal Hoque, KK., Ahmed, K., Bui, FM., Lio, P., Moni, MA. and Al-Zahrani, FA., 2023. StackFBAs: Detection of fetal brain abnormalities using CNN with stacking strategy from MRI images Journal of King Saud University - Computer and Information Sciences, v. 35
Doi: http://doi.org/10.1016/j.jksuci.2023.101647

Charoenkwan, P., Pipattanaboon, C., Nantasenamat, C., Hasan, MM., Moni, MA., Lio', P. and Shoombuatong, W., 2023. PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning. Comput Biol Med, v. 152
Doi: http://doi.org/10.1016/j.compbiomed.2022.106368

Zhu, M., Kobalczyk, K., Petrovic, A., Nikolic, M., Schaar, MVD., Delibasic, B. and Liò, P., 2023. Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces. CoRR, v. abs/2311.10051

Bisercic, A., Nikolic, M., Schaar, MVD., Delibasic, B., Liò, P. and Petrovic, A., 2023. Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models. CoRR, v. abs/2306.05052

Lu, X., Yang, F., Zou, L., Lio, P. and Hui, P., 2023. An LTE Authentication and Key Agreement Protocol Based on the ECC Self-Certified Public Key IEEE/ACM Transactions on Networking, v. 31
Doi: 10.1109/TNET.2022.3207360

Chen, J., Wang, Y., Bodnar, C., Ying, R., Lio, P. and Wang, Y., 2023. Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation. CoRR, v. abs/2311.05767

Giannini, F., Fioravanti, S., Keskin, O., Lupidi, AM., Magister, LC., Lió, P. and Barbiero, P., 2023. Interpretable Graph Networks Formulate Universal Algebra Conjectures Advances in Neural Information Processing Systems, v. 36

Villaforesta, AFD., Magister, LC., Barbiero, P. and Liò, P., 2023. Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians. CoRR, v. abs/2312.02225

Wang, Z., Gao, Q., Yi, X., Zhang, X., Zhang, Y., Zhang, D., Liò, P., Bain, C., Bassed, R., Li, S., Guo, Y., Imoto, S., Yao, J., Daly, RJ. and Song, J., 2023. Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images. Comput Methods Programs Biomed, v. 241
Doi: 10.1016/j.cmpb.2023.107733

Sun, Z., Harit, A., Cristea, AI., Wang, J. and Lio, P., 2023. MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model AI Open, v. 4
Doi: http://doi.org/10.1016/j.aiopen.2023.10.002

Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Liò, P., Maggini, M. and Melacci, S., 2023. Logic Explained Networks Artificial Intelligence, v. 314
Doi: http://doi.org/10.1016/j.artint.2022.103822

Yang, J., Li, X-X., Liu, F., Nie, D., Lio, P., Qi, H. and Shen, D., 2023. Fast Multi-Contrast MRI Acquisition by Optimal Sampling of Information Complementary to Pre-Acquired MRI Contrast. IEEE Trans Med Imaging, v. 42
Doi: 10.1109/TMI.2022.3227262

Bazaga, A., Liò, P. and Micklem, G., 2023. SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation. CoRR, v. abs/2310.18376

Zhu, M., Stanivuk, S., Petrovic, A., Nikolic, M. and Lio, P., 2023. Incorporating LLM Priors into Tabular Learners. CoRR, v. abs/2311.11628

Wölflein, G., Magister, LC., Liò, P., Harrison, DJ. and Arandjelovic, O., 2023. Deep Multiple Instance Learning with Distance-Aware Self-Attention. CoRR, v. abs/2305.10552

Faruqui, N., Yousuf, MA., Whaiduzzaman, M., Azad, AKM., Alyami, SA., Liò, P., Kabir, MA. and Moni, MA., 2023. SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization Electronics (Switzerland), v. 12
Doi: 10.3390/electronics12173541

Petrović, A., Nikolić, M., Bugarić, U., Delibašić, B. and Lio, P., 2023. Controlling highway toll stations using deep learning, queuing theory, and differential evolution Engineering Applications of Artificial Intelligence, v. 119
Doi: http://doi.org/10.1016/j.engappai.2022.105683

Duta, I., Silvestri, F., Cassarà, G. and Liò, P., 2023. Sheaf Hypergraph Networks Advances in Neural Information Processing Systems, v. 36

Lachi, V., Dimitri, GM., Stefano, AD., Liò, P., Bianchini, M. and Mocenni, C., 2023. Impact of the Covid 19 outbreaks on the italian twitter vaccination debat: a network based analysis. CoRR, v. abs/2306.02838

Campbell, A., Spasov, S., Toschi, N. and Liò, P., 2023. DBGDGM: Dynamic Brain Graph Deep Generative Model Proceedings of Machine Learning Research, v. 227

Nayan, SI., Rahman, MH., Hasan, MM., Raj, SMRH., Almoyad, MAA., Liò, P. and Moni, MA., 2023. Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities. Life Sci, v. 335
Doi: http://doi.org/10.1016/j.lfs.2023.122244

Telyatnikov, L., Bucarelli, MS., Bernárdez, G., Zaghen, O., Scardapane, S. and Lio, P., 2023. Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design. CoRR, v. abs/2310.07684

Xuanyuan, H., Barbiero, P., Georgiev, D., Magister, LC. and Liò, P., 2023. Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, v. 37

Giusti, L., Reu, T., Ceccarelli, F., Bodnar, C. and Liò, P., 2023. CIN++: Enhancing Topological Message Passing. CoRR, v. abs/2306.03561

Sathyanarayanan, A., Mueller, TT., Ali Moni, M., Schueler, K., ECNP TWG Network members, , Baune, BT., Lio, P., Mehta, D., Baune, BT., Dierssen, M., Ebert, B., Fabbri, C., Fusar-Poli, P., Gennarelli, M., Harmer, C., Howes, OD., Janzing, JGE., Lio, P., Maron, E., Mehta, D., Minelli, A., Nonell, L., Pisanu, C., Potier, M-C., Rybakowski, F., Serretti, A., Squassina, A., Stacey, D., van Westrhenen, R. and Xicota, L., 2023. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol, v. 69
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Dominici, G., Barbiero, P., Magister, LC., Liò, P. and Simidjievski, N., 2023. SHARCS: Shared Concept Space for Explainable Multimodal Learning. CoRR, v. abs/2307.00316

Li, Z., Zhao, X., Shen, M., Stan, G-B., Liò, P. and Zhao, Y., 2023. Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs. CoRR, v. abs/2306.05108

Barbiero, P., Fioravanti, S., Giannini, F., Tonda, A., Liò, P. and Lavore, ED., 2023. Categorical Foundations of Explainable AI: A Unifying Formalism of Structures and Semantics. CoRR, v. abs/2304.14094

Lu, X., Liu, C., Zhu, S., Mao, Y., Lio, P. and Hui, P., 2023. RLPTO: A Reinforcement Learning-Based Performance-Time Optimized Task and Resource Scheduling Mechanism for Distributed Machine Learning IEEE Transactions on Parallel and Distributed Systems, v. 34
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Zou, X., Zhao, X., Liò, P. and Zhao, Y., 2023. Will More Expressive Graph Neural Networks do Better on Generative Tasks? Proceedings of Machine Learning Research, v. 231

Waqas, M., Aziz, S., Liò, P., Khan, Y., Ali, A., Iqbal, A., Khan, F. and Almajhdi, FN., 2023. Immunoinformatics design of multivalent epitope vaccine against monkeypox virus and its variants using membrane-bound, enveloped, and extracellular proteins as targets. Front Immunol, v. 14
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Bujel, K., Gideoni, Y., Joshi, CK. and Liò, P., 2023. Group Invariant Global Pooling. CoRR, v. abs/2305.19207

Ambags, EL., Capitoli, G., Imperio, VL., Provenzano, M., Nobile, MS. and Liò, P., 2023. Assisting clinical practice with fuzzy probabilistic decision trees. CoRR, v. abs/2304.07788

de Ocáriz Borde, HS., Kazi, A., Barbero, F. and Liò, P., 2023. LATENT GRAPH INFERENCE USING PRODUCT MANIFOLDS 11th International Conference on Learning Representations, ICLR 2023,

Jiang, Y., Ding, Q., Wang, YG., Liò, P. and Zhang, X., 2023. VISION GRAPH U-NET: GEOMETRIC LEARNING ENHANCED ENCODER FOR MEDICAL IMAGE SEGMENTATION AND RESTORATION Inverse Problems and Imaging, v. 2023
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Kidwai, S., Barbiero, P., Meijerman, I., Tonda, A., Perez-Pardo, P., Lio, P., van der Maitland-Zee, AH., Oberski, DL., Kraneveld, AD. and Lopez-Rincon, A., 2023. A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate-to-severe asthma. Clin Transl Allergy, v. 13
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Caso, F., Trappolini, G., Bacciu, A., Liò, P. and Silvestri, F., 2023. Renormalized Graph Neural Networks. CoRR, v. abs/2306.00707

Bergna, R., Opolka, FL., Liò, P. and Hernández-Lobato, JM., 2023. Graph Neural Stochastic Differential Equations. CoRR, v. abs/2308.12316

Huang, K. and Liò, P., 2023. An Effective Universal Polynomial Basis for Spectral Graph Neural Networks. CoRR, v. abs/2311.18177

COVID-19 Host Genetics Initiative, , 2023. A second update on mapping the human genetic architecture of COVID-19. Nature, v. 621
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2022 (Accepted for publication)

Lishkova, Y., Scherer, P., Ridderbusch, S., Jamnik, M., Liò, P., Ober-Blöbaum, S. and Offen, C., 2022 (Accepted for publication). Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery IFAC-PapersOnLine, v. 56
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2022

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Coggan, H., Andres Terre, H. and Liò, P., 2022. A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth. Front Big Data, v. 5
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Azevedo, T., Bethlehem, RAI., Whiteside, D., Swaddiwudhipong, N., Rowe, J., Lió, P. and Rittman, T., 2022. Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank
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Lu, X., Xue, A., Lio, P. and Hui, P., 2022. Intelligent Decision Making Based on the Combination of Deep Reinforcement Learning and an Influence Map Applied Sciences (Switzerland), v. 12
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Patel, S. and Lio, P., 2022. Efficacy, Safety, and Applications of Skin Protectants. J Drugs Dermatol, v. 21
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Christensen, CN., Lu, M., Ward, EN., Liò, P. and Kaminski, CF., 2022. Spatio-temporal Vision Transformer for Super-resolution Microscopy. CoRR, v. abs/2203.00030

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Zarlenga, ME., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Liò, P. and Jamnik, M., 2022. Concept Embedding Models. CoRR, v. abs/2209.09056

Buffelli, D., Liò, P. and Vandin, F., 2022. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks

Charoenkwan, P., Schaduangrat, N., Lio, P., Moni, MA., Chumnanpuen, P. and Shoombuatong, W., 2022. iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides. ACS Omega, v. 7
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Lu, M., Christensen, C., Weber, J., Konno, T., Läubli, N., Scherer, K., Avezov, E., Lio, P., Lapkin, A., Kaminski Schierle, G. and Kaminski, C., 2022. ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology for video-rate super-resolution imaging
Doi: http://doi.org/10.1101/2022.05.17.492189

Ahamad, MM., Aktar, S., Uddin, MJ., Rashed-Al-Mahfuz, M., Azad, AKM., Uddin, S., Alyami, SA., Sarker, IH., Khan, A., Liò, P., Quinn, JMW. and Moni, MA., 2022. Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity. Healthcare (Basel), v. 11
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Christensen, CN., Lu, M., Ward, EN., Lio, P. and Kaminski, CF., 2022. Spatio-temporal Vision Transformer for Super-resolution Microscopy

Schaduangrat, N., Anuwongcharoen, N., Moni, MA., Lio', P., Charoenkwan, P. and Shoombuatong, W., 2022. StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy. Sci Rep, v. 12
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Goh, CWJ., Bodnar, C. and Liò, P., 2022. Simplicial Attention Networks. CoRR, v. abs/2204.09455

Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Lio, P., 2022. Encoding Concepts in Graph Neural Networks

Charoenkwan, P., Nantasenamat, C., Hasan, MM., Moni, MA., Lio', P., Manavalan, B. and Shoombuatong, W., 2022. StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. Methods, v. 204
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Shadbahr, T., Roberts, M., Stanczuk, J., Gilbey, JD., Teare, P., Dittmer, S., Thorpe, M., Torné, RV., Sala, E., Lió, P., Patel, M., Collaboration, A-C., Rudd, JHF., Mirtti, T., Rannikko, A., Aston, JAD., Tang, J. and Schönlieb, C-B., 2022. Classification of datasets with imputed missing values: does imputation quality matter? CoRR, v. abs/2206.08478

Ahmad, S., Charoenkwan, P., Quinn, JMW., Moni, MA., Hasan, MM., Lio', P. and Shoombuatong, W., 2022. SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins. Sci Rep, v. 12
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Dimitri, GM., Meoni, G., Tenori, L., Luchinat, C. and Lió, P., 2022. NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints Applied Sciences (Switzerland), v. 12
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Liu, L., Huang, Z., Liò, P., Schönlieb, C-B. and Avilés-Rivero, AI., 2022. PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation. CoRR, v. abs/2203.05684

Ayorinde, JOO., Citterio, F., Landrò, M., Peruzzo, E., Islam, T., Tilley, S., Taylor, G., Bardsley, V., Liò, P., Samoshkin, A. and Pettigrew, GJ., 2022. Artificial Intelligence You Can Trust: What Matters Beyond Performance When Applying Artificial Intelligence to Renal Histopathology? J Am Soc Nephrol, v. 33
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Buterez, D., Bica, I., Tariq, I., Andrés-Terré, H. and Liò, P., 2022. CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks. Bioinformatics, v. 38
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Chen, Y., Schönlieb, C-B., Liò, P., Leiner, T., Dragotti, PL., Wang, G., Rueckert, D., Firmin, DN. and Yang, G., 2022. AI-Based Reconstruction for Fast MRI - A Systematic Review and Meta-Analysis. Proc. IEEE, v. 110

Meoni, G., Tenori, L., Schade, S., Licari, C., Pirazzini, C., Bacalini, MG., Garagnani, P., Turano, P., PROPAG-AGEING Consortium, , Trenkwalder, C., Franceschi, C., Mollenhauer, B. and Luchinat, C., 2022. Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients. NPJ Parkinsons Dis, v. 8
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Goh, CWJ., Bodnar, C. and Liò, P., 2022. Simplicial Attention Networks

Borgheresi, R., Barucci, A., Colantonio, S., Aghakhanyan, G., Assante, M., Bertelli, E., Carlini, E., Carpi, R., Caudai, C., Cavallero, D., Cioni, D., Cirillo, R., Colcelli, V., Dell'Amico, A., Di Gangi, D., Erba, PA., Faggioni, L., Falaschi, Z., Gabelloni, M., Gini, R., Lelii, L., Liò, P., Lorito, A., Lucarini, S., Manghi, P., Mangiacrapa, F., Marzi, C., Mazzei, MA., Mercatelli, L., Mirabile, A., Mungai, F., Miele, V., Olmastroni, M., Pagano, P., Paiar, F., Panichi, G., Pascali, MA., Pasquinelli, F., Shortrede, JE., Tumminello, L., Volterrani, L., Neri, E. and NAVIGATOR Consortium Group, , 2022. NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients. Eur Radiol Exp, v. 6
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Viñas, R., Andrés-Terré, H., Liò, P. and Bryson, K., 2022. Adversarial generation of gene expression data. Bioinform., v. 38

Charoenkwan, P., Schaduangrat, N., Hasan, MM., Moni, MA., Lió, P. and Shoombuatong, W., 2022. Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins. EXCLI J, v. 21
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Barbero, F., Bodnar, C., Borde, HSDO., Bronstein, M., Veličković, P. and Liò, P., 2022. Sheaf Neural Networks with Connection Laplacians

Charoenkwan, P., Schaduangrat, N., Lio', P., Moni, MA., Shoombuatong, W. and Manavalan, B., 2022. Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. iScience, v. 25
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Lu, X., Liao, Y., Liu, C., Lio, P. and Hui, P., 2022. Heterogeneous Model Fusion Federated Learning Mechanism Based on Model Mapping IEEE Internet of Things Journal, v. 9
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Bongini, P., Scarselli, F., Bianchini, M., Dimitri, GM., Pancino, N. and Liò, P., 2022. Modular multi-source prediction of drug side-effects with DruGNN. CoRR, v. abs/2202.08147

Chen, Y., Schonlieb, CB., Lio, P., Leiner, T., Dragotti, PL., Wang, G., Rueckert, D., Firmin, D. and Yang, G., 2022. AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis Proceedings of the IEEE, v. 110
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Jain, A., Mishra, C. and Liò, P., 2022. A physics-informed search for metric solutions to Ricci flow, their embeddings, and visualisation. CoRR, v. abs/2212.05892

Liu, L., Huang, Z., Liò, P., Schönlieb, C-B. and Aviles-Rivero, AI., 2022. PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation

Huang, J., Fang, Y., Nan, Y., Wu, H., Wu, Y., Gao, Z., Li, Y., Wang, Z., Lio, P., Rueckert, D., Eldar, YC. and Yang, G., 2022. Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

Mitchell, H., Norcliffe, A. and Liò, P., 2022. Learning Feynman Diagrams using Graph Neural Networks. CoRR, v. abs/2211.15348

Yi, K., Chen, J., Wang, YG., Zhou, B., Liò, P., Fan, Y. and Hamann, J., 2022. Approximate Equivariance SO(3) Needlet Convolution

Charoenkwan, P., Schaduangrat, N., Lio', P., Moni, MA., Manavalan, B. and Shoombuatong, W., 2022. NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides. Comput Biol Med, v. 148
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Margeloiu, A., Simidjievski, N., Lio', P. and Jamnik, M., 2022. Graph-Conditioned MLP for High-Dimensional Tabular Biomedical Data. CoRR, v. abs/2211.06302

2021 (Published online)

Tailor, SA., Opolka, FL., Liò, P. and Lane, ND., 2021 (Published online). Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions arxiv,

2021 (No publication date)

Christensen, CN., Ward, E., Lio, P. and Kaminski, C., 2021 (No publication date). ML-SIM: Universal reconstruction of structured illumination microscopy images using transfer learning Biomedical Optics Express,
Doi: http://doi.org/10.1364/boe.414680

2021 (Accepted for publication)

Banerjee, S., Lio, P., Jones, P. and Cardinal, R., 2021 (Accepted for publication). A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness npj Schizophrenia,
Doi: http://doi.org/10.1038/s41537-021-00191-y

Scherer, P., Trębacz, M., Simidjievski, N., Viñas, R., Shams, Z., Terre, HA., Jamnik, M. and Liò, P., 2021 (Accepted for publication). Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. Bioinformatics,
Doi: http://doi.org/10.1093/bioinformatics/btab830

Charoenkwan, P., Nantasenamat, C., Hasan, MM., Moni, MA., Lio', P. and Shoombuatong, W., 2021 (Accepted for publication). iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features. International Journal of Molecular Sciences, v. 22
Doi: http://doi.org/10.3390/ijms22168958

Viñas, R., Andrés-Terré, H., Liò, P. and Bryson, K., 2021 (Accepted for publication). Adversarial generation of gene expression data. Bioinformatics,
Doi: http://doi.org/10.1093/bioinformatics/btab035

Viñas Torné, R., Azevedo, T., R. Gamazon, E. and Liò, P., 2021 (Accepted for publication). Deep Learning Enables Fast and Accurate Imputation of Gene Expression Frontiers in Genetics,

Dimitri, GM., Beqiri, E., Placek, MM., Czosnyka, M., Stocchetti, N., Ercole, A., Smielewski, P., Lio, P. and CENTER-TBI collaborators, , 2021 (Accepted for publication). Modelling brain-heart cross-talks information in Traumatic Brain Injury patients Neurocritical Care,
Doi: http://doi.org/10.1007/s12028-021-01353-7

Barbiero, P., Torné, RV. and Lió, P., 2021 (Accepted for publication). Graph representation forecasting of patient's medical conditions: towards a digital twin Frontiers in Genetics,

Bodnar, C., Cangea, C. and Lio, P., 2021 (Accepted for publication). Deep Graph Mapper: Seeing Graphs through the Neural Lens Frontiers in Big Data,

Cvejic, A., Tangherloni, A. and Liò, P., 2021 (Accepted for publication). Analysis of single-cell RNA sequencing data based on autoencoders BMC Bioinformatics,

Roberts, M., Driggs, D., Thorpe, MATTHEW., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A., Etmann, C., McCague, C., Beer, L., Weir-McCall, J., Teng, Z., Gkrania-Klotsas, E., Collaboration, AIX-COVNET., Rudd, J., Sala, E. and Schoenlieb, C-B., 2021 (Accepted for publication). Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans Nature Machine Intelligence,

2021

Zhu, M., Lió, P. and Moss, J., 2021. Modular Neural Ordinary Differential Equations. CoRR, v. abs/2109.07359

Barbiero, P., Ciravegna, G., Giannini, F., Lió, P., Gori, M. and Melacci, S., 2021. Entropy-based Logic Explanations of Neural Networks In Proceedings of the AAAI Conference on Artificial Intelligence, v. 36

Magister, LC., Kazhdan, D., Singh, V. and Liò, P., 2021. GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks

Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S. and Liò, P., 2021. 3D Infomax improves GNNs for Molecular Property Prediction 39th International Conference on Machine Learning (ICML 2022),

Deasy, J., Simidjievski, N. and Liò, P., 2021. Heavy-tailed denoising score matching. CoRR, v. abs/2112.09788

Bodnar, C., Frasca, F., Otter, N., Wang, YG., Liò, P., Montúfar, G. and Bronstein, M., 2021. Weisfeiler and Lehman Go Cellular: CW Networks Advances in Neural Information Processing Systems, v. 4

Yang, J., Li, X-X., Liu, F., Nie, D., Liò, P., Qi, H. and Shen, D., 2021. Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI. CoRR, v. abs/2111.06400

Day, B., Viñas, R., Simidjievski, N. and Liò, P., 2021. Attentional Meta-learners for Few-shot Polythetic Classification

Opolka, FL., Zhi, Y-C., Liò, P. and Dong, X., 2021. Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets

Moss, J., Opolka, FL., Dumitrascu, B. and Lió, P., 2021. Approximate Latent Force Model Inference. CoRR, v. abs/2109.11851

Pirazzini, C., Azevedo, T., Baldelli, L., Bartoletti-Stella, A., Calandra-Buonaura, G., Dal Molin, A., Dimitri, GM., Doykov, I., Gómez-Garre, P., Hägg, S., Hällqvist, J., Halsband, C., Heywood, W., Jesús, S., Jylhävä, J., Kwiatkowska, KM., Labrador-Espinosa, MA., Licari, C., Maturo, MG., Mengozzi, G., Meoni, G., Milazzo, M., Periñán-Tocino, MT., Ravaioli, F., Sala, C., Sambati, L., Schade, S., Schreglmann, S., Spasov, S., Tenori, L., Williams, D., Xumerle, L., Zago, E., Bhatia, KP., Capellari, S., Cortelli, P., Garagnani, P., Houlden, H., Liò, P., Luchinat, C., Delledonne, M., Mills, K., Mir, P., Mollenhauer, B., Nardini, C., Pedersen, NL., Provini, F., Strom, S., Trenkwalder, C., Turano, P., Bacalini, MG., Franceschi, C. and PROPAG-AGEING Consortium, , 2021. A geroscience approach for Parkinson's disease: Conceptual framework and design of PROPAG-AGEING project. Mech Ageing Dev, v. 194
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Fanfani, V., Vinas Torne, R., Lio', P. and Stracquadanio, G., 2021. Discovering cancer driver genes and pathways using stochastic block model graph neural networks
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King, J., Torné, RV., Campbell, A. and Liò, P., 2021. An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution. CoRR, v. abs/2109.14994

Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Lió, P., Maggini, M. and Melacci, S., 2021. Logic Explained Networks Artificial Intelligence, 103822, 2022,

Chen, Y., Schönlieb, C-B., Liò, P., Leiner, T., Dragotti, PL., Wang, G., Rueckert, D., Firmin, DN. and Yang, G., 2021. AI-based Reconstruction for Fast MRI - A Systematic Review and Meta-analysis. CoRR, v. abs/2112.12744

Caccuri, F., D'Ursi, P., Uggeri, M., Bugatti, A., Mazzuca, P., Zani, A., Filippini, F., Salmona, M., Ribatti, D., Slevin, M., Orro, A., Lu, W., Liò, P., Gallo, RC. and Caruso, A., 2021. Evolution toward beta common chain receptor usage links the matrix proteins of HIV-1 and its ancestors to human erythropoietin. Proc Natl Acad Sci U S A, v. 118
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Nain, Z., Rana, HK., Liò, P., Islam, SMS., Summers, MA. and Moni, MA., 2021. Pathogenetic profiling of COVID-19 and SARS-like viruses. Briefings Bioinform., v. 22

Lu, X., Fu, S., Jiang, C. and Lió, P., 2021. A Fine-Grained IoT Data Access Control Scheme Combining Attribute-Based Encryption and Blockchain. Secur. Commun. Networks, v. 2021

D'Agostino, D., Liò, P., Aldinucci, M. and Merelli, I., 2021. Advantages of using graph databases to explore chromatin conformation capture experiments. BMC Bioinformatics, v. 22
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Lipov, A. and Liò, P., 2021. A Multiscale Graph Convolutional Network Using Hierarchical Clustering Advances in Intelligent Systems and Computing, v. 1364 AISC
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Jamasb, AR., Day, B., Cangea, C., Liò, P. and Blundell, TL., 2021. Deep Learning for Protein-Protein Interaction Site Prediction. Methods Mol Biol, v. 2361
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Bagnoli, F., Lorini, D. and Lió, P., 2021. Modeling Social Groups, Policies and Cognitive Behavior in COVID-19 Epidemic Phases. Basic Scenarios Substantia, v. 4
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Zhou, B., Liu, X., Liu, Y., Huang, Y., Liò, P. and Wang, Y., 2021. Spectral Transform Forms Scalable Transformer

Zheng, X., Zhou, B., Gao, J., Wang, YG., Lio, P., Li, M. and Montufar, G., 2021. How Framelets Enhance Graph Neural Networks INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, v. 139

Chen, K., Xu, H., Lei, Y., Lio, P., Li, Y., Guo, H. and Ali Moni, M., 2021. Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance. Brief Bioinform, v. 22
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Georgiev, D., Barbiero, P., Kazhdan, D., Veličković, P. and Liò, P., 2021. Algorithmic Concept-based Explainable Reasoning

Lu, X., Fu, S., Jiang, C. and Lio, P., 2021. A Fine-Grained IoT Data Access Control Scheme Combining Attribute-Based Encryption and Blockchain Security and Communication Networks, v. 2021
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Yang, J., Li, X-X., Liu, F., Nie, D., Lio, P., Qi, H. and Shen, D., 2021. Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI

Beaini, D., Passaro, S., Letourneau, V., Hamilton, WL., Corso, G. and Lio, P., 2021. Directional Graph Networks INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, v. 139

Day, B., Norcliffe, A., Moss, J. and Liò, P., 2021. Meta-learning using privileged information for dynamics

Qendro, L., Campbell, A., Liò, P. and Mascolo, C., 2021. High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm

Barbiero, P., Viñas Torné, R. and Lió, P., 2021. Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin. Front Genet, v. 12
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Rashed-Al-Mahfuz, M., Moni, MA., Lio', P., Islam, SMS., Berkovsky, S., Khushi, M. and Quinn, JMW., 2021. Deep convolutional neural networks based ECG beats classification to diagnose cardiovascular conditions. Biomed Eng Lett, v. 11
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Shankar, V., Tibshirani, R. and Zare, RN., 2021. MassExplorer: a computational tool for analyzing desorption electrospray ionization mass spectrometry data. Bioinformatics,
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Zhu, J., Tan, C., Yang, J., Yang, G. and Lio', P., 2021. Arbitrary Scale Super-Resolution for Medical Images. Int J Neural Syst, v. 31
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Rocheteau, E., Liò, P. and Hyland, S., 2021. Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit ACM CHIL 2021 - Proceedings of the 2021 ACM Conference on Health, Inference, and Learning,
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Baldelli, L., Schade, S., Jesús, S., Schreglmann, SR., Sambati, L., Gómez-Garre, P., Halsband, C., Calandra-Buonaura, G., Adarmes-Gómez, AD., Sixel-Döring, F., Zenesini, C., Pirazzini, C., Garagnani, P., Bacalini, MG., Bhatia, KP., Cortelli, P., Mollenhauer, B., Franceschi, C., PROPAG-AGEING consortium, , Mir, P., Trenkwalder, C. and Provini, F., 2021. Heterogeneity of prodromal Parkinson symptoms in siblings of Parkinson disease patients. NPJ Parkinsons Dis, v. 7
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Scata, M., Di Stefano, A., La Corte, A. and Lio, P., 2021. A Multiplex Social Contagion Dynamics Model to Shape and Discriminate D2D Content Dissemination IEEE Transactions on Cognitive Communications and Networking, v. 7
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Zubić, N. and Liò, P., 2021. An Effective Loss Function for Generating 3D Models from Single 2D Image Without Rendering IFIP Advances in Information and Communication Technology, v. 627
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Zhu, J., Tan, C., Yang, J., Yang, G. and Lio', P., 2021. MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution

Bardozzo, F., Lió, P. and Tagliaferri, R., 2021. Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks. Bioinformatics, v. 37
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Deasy, J., Simidjievski, N. and Liò, P., 2021. Heavy-tailed denoising score matching

Azevedo, T., Dimitri, GM., Lió, P. and Gamazon, ER., 2021. Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits. NPJ Syst Biol Appl, v. 7
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Islam, MR., Moni, MA., Islam, MM., Rashed-Al-Mahfuz, M., Islam, MS., Hasan, MK., Hossain, MS., Ahmad, M., Uddin, S., Azad, A., Alyami, SA., Ahad, MAR. and Lio, P., 2021. Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques IEEE Access, v. 9
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Zhu, M., Lio, P. and Moss, J., 2021. Modular Neural Ordinary Differential Equations

COVID-19 Host Genetics Initiative, , 2021. Mapping the human genetic architecture of COVID-19. Nature, v. 600
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Zhou, B., Liu, X., Liu, Y., Huang, Y., Liò, P. and Wang, Y., 2021. Spectral Transform Forms Scalable Transformer. CoRR, v. abs/2111.07602

Weber, JM., Lindenmeyer, CP., Liò, P. and Lapkin, AA., 2021. Teaching sustainability as complex systems approach: a sustainable development goals workshop International Journal of Sustainability in Higher Education, v. 22
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Ma, Z., Xuan, J., Wang, YG., Li, M. and LiÃ², P., 2021. tion Processing Systems vol 33 ed H Larochelle, M Ranzato, R Hadsell, M F Balcan and H Lin (New York: Curran Associates) pp 16421–33. ... Journal of Statistical Mechanics: Theory and Experiment, v. 2021
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Castiglione, F., Deb, D., Srivastava, AP., Liò, P. and Liso, A., 2021. From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling. Front Immunol, v. 12
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van Der Schaar, M., Alaa, AM., Floto, A., Gimson, A., Scholtes, S., Wood, A., McKinney, E., Jarrett, D., Lio, P. and Ercole, A., 2021. How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 Machine Learning, v. 110
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Iuliano, A., Occhipinti, A., Angelini, C., De Feis, I. and Liò, P., 2021. Cosmonet: An r package for survival analysis using screening-network methods Mathematics, v. 9
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Lu, X., Wang, F., Jiang, C. and Lio, P., 2021. A universal malicious documents static detection framework based on feature generalization Applied Sciences (Switzerland), v. 11
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Bardozzo, F., Lió, P. and Tagliaferri, R., 2021. Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks. Bioinform., v. 37

Vecchio, AD., Deac, A., Liò, P. and Veličković, P., 2021. Neural message passing for joint paratope-epitope prediction

Amor, A., Lio', P., Singh, V., Torné, RV. and Terre, HA., 2021. Graph Representation Learning on Tissue-Specific Multi-Omics

2020 (Published online)

Trębacz, M., Shams, Z., Jamnik, M., Scherer, P., Simidjievski, N., Terre, HA. and Liò, P., 2020 (Published online). Using ontology embeddings for structural inductive bias in gene expression data analysis arxiv,

2020 (Accepted for publication)

Dimitri, GM., Beqiri, E., Czosnyka, M., Ercole, A., Smielewski, P., Liò, P. and CENTER-TBI High Resolution Sub- Study Participants and Investigators, , 2020 (Accepted for publication). Analysing cardio-cerebral crosstalks in an adult cohort from CENTER-TBI Acta Neurochirurgica: Supplementum,
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Andres Terre, H., Cvejic, A. and Lio, P., 2020 (Accepted for publication). Unsupervised generative and graph representation learning for modelling cell differentiation Scientific Reports,

Deasy, J., Liò, P. and Ercole, A., 2020 (Accepted for publication). Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation Scientific Reports,
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Christensen, CN., Ward, EN., Lio, P. and Kaminski, CF., 2020 (Accepted for publication). ML-SIM: A deep neural network for reconstruction of structured illumination microscopy images arXiv,

2020

Scherer, P. and Lio, P., 2020. Learning distributed representations of graphs with Geo2DR

Lu, X., Wang, X., Lio, P. and Hui, P., 2020. DADIM: A distance adjustment dynamic influence map model Future Generation Computer Systems, v. 112
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Tan, C., Zhu, J. and Lio’, P., 2020. Arbitrary scale super-resolution for brain MRI images IFIP Advances in Information and Communication Technology, v. 583 IFIP
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Jamasb, A., Viñas, R., Ma, E., Harris, C., Huang, K., Hall, D., Lió, P. and Blundell, T., 2020. Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks
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Rollins, CPE., Garrison, J., Arribas, M., Seyedsalehi, A., Li, Z., Chan, RCK., Yang, J., Wang, D., Lio, P., Yan, C., Yi, Z-H., Cachia, A., Upthegrove, R., Deakin, B., Simons, J., Murray, G. and Suckling, J., 2020. The neurodevelopment of anomalous perception: Evidence in cortical folding patterns for prenatal predispositions to hallucinations in schizophrenia
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Flood, PDL., Viñas, R. and Liò, P., 2020. Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction

Cangea, C., Velickovic, P. and Liò, P., 2020. XFlow: Cross-Modal Deep Neural Networks for Audiovisual Classification. IEEE Trans. Neural Networks Learn. Syst., v. 31
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Rakowski, AG., Veličković, P., Dall'Ara, E. and Liò, P., 2020. ChronoMID-Cross-modal neural networks for 3-D temporal medical imaging data. PLoS One, v. 15
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Stankevičiūtė, K., Azevedo, T., Campbell, A., Bethlehem, R. and Liò, P., 2020. Population Graph GNNs for Brain Age Prediction
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Spasov, S., Stefano, AD., Lio, P. and Tang, J., 2020. GRADE: Graph Dynamic Embedding

Wichitwechkarn, V., Day, B., Bodnar, C., Wales, M. and Liò, P., 2020. The Role of Isomorphism Classes in Multi-Relational Datasets

Müller, TT. and Lio, P., 2020. PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases. Front Artif Intell, v. 3
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Kazhdan, D., Dimanov, B., Jamnik, M., Liò, P. and Weller, A., 2020. Now You See Me (CME): Concept-based Model Extraction. CoRR, v. abs/2010.13233

Lu, X., Liao, Y., Lio, P. and Hui, P., 2020. Privacy-preserving asynchronous federated learning mechanism for edge network computing IEEE Access, v. 8
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Deasy, J., Simidjievski, N. and Liò, P., 2020. Constraining variational inference with geometric Jensen-Shannon divergence Advances in Neural Information Processing Systems, v. 2020-December

Day, B., Cangea, C., Jamasb, AR. and Liò, P., 2020. Message Passing Neural Processes

Di Stefano, A., Scatá, M., Attanasio, B., La Corte, A., Lió, P. and Das, SK., 2020. A Novel Methodology for designing Policies in Mobile Crowdsensing Systems Pervasive and Mobile Computing, v. 67
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Rana, HK., Akhtar, MR., Islam, MB., Ahmed, MB., Lió, P., Huq, F., Quinn, JMW. and Moni, MA., 2020. Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression. Sci Rep, v. 10
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Kazhdan, D., Shams, Z. and Lio, P., 2020. MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library Proceedings of the International Joint Conference on Neural Networks,
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Wang, D., Jamnik, M. and Lio, P., 2020. ABSTRACT DIAGRAMMATIC REASONING WITH MULTIPLEX GRAPH NETWORKS 8th International Conference on Learning Representations, ICLR 2020,

Spasov, SE. and Liò, P., 2020. Dynamic neural network channel execution for efficient training 30th British Machine Vision Conference 2019, BMVC 2019,

Maria, ED., Despeyroux, J., Felty, A., Liò, P., Olarte, C. and Bahrami, A., 2020. Computational Logic for Biomedicine and Neurosciences

Zhao, Y., Wang, D., Bates, D., Mullins, R., Jamnik, M. and Lio, P., 2020. Learned Low Precision Graph Neural Networks

Merelli, I., Liò, P., Kotenko, I. and D'Agostino, D., 2020. Latest advances in parallel, distributed, and network-based processing Concurrency and Computation: Practice and Experience, v. 32
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Del Prete, E., Facchiano, A. and Liò, P., 2020. Bioinformatics methodologies for coeliac disease and its comorbidities. Brief Bioinform, v. 21
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Yeghikyan, G., Opolka, FL., Nanni, M., Lepri, B. and Lio', P., 2020. Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks

Scherer, P., Trȩbacz, M., Simidjievski, N., Shams, Z., Terre, HA., Liò, P. and Jamnik, M., 2020. Incorporating network based protein complex discovery into automated model construction

Karavias, V., Day, B. and Liò, P., 2020. Uncertainty in Neural Relational Inference Trajectory Reconstruction

Moss, J. and Lió, P., 2020. Gene Regulatory Network Inference with Latent Force Models

Lu, X., Zhang, S., Hui, P. and Lio, P., 2020. Continuous authentication by free-text keystroke based on CNN and RNN Computers and Security, v. 96
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Campbell, A. and Liò, P., 2020. tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder

Buterez, D., Bica, I., Tariq, I., Andrés-Terré, H. and Liò, P., 2020. CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks
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Georgiev, D. and Liò, P., 2020. Neural Bipartite Matching

Barbiero, P. and Lió, P., 2020. The computational Patient has diabetes and a COVID
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Bodnar, C., Day, B. and Lió, P., 2020. Proximal distilled evolutionary reinforcement learning AAAI 2020 - 34th AAAI Conference on Artificial Intelligence,

Stefano, AD., Scatà, M., Attanasio, B., Corte, AL., Lió, P. and Das, SK., 2020. A Novel Methodology for designing Policies in Mobile Crowdsensing Systems. Pervasive Mob. Comput., v. 67
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Lu, X., Zhou, X., Wang, W., Lio, P. and Hui, P., 2020. Domain-oriented topic discovery based on features extraction and topic clustering IEEE Access, v. 8
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Viñas, R., Azevedo, T., Gamazon, E. and Liò, P., 2020. Gene Expression Imputation with Generative Adversarial Imputation Nets
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Azevedo, T., Passamonti, L., Liò, P. and Toschi, N., 2020. Towards a predictive spatio-temporal representation of brain data

Azevedo, T., Dimitri, GM., Lio, P. and Gamazon, E., 2020. Multilayer modelling and analysis of the human transcriptome
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Glass, S., Spasov, S. and Liò, P., 2020. RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow

Nain, Z., Rana, HK., Liò, P., Islam, SMS., Summers, MA. and Moni, MA., 2020. Pathogenetic profiling of COVID-19 and SARS-like viruses. Briefings in Bioinformatics,
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Rocheteau, E., Liò, P. and Hyland, S., 2020. Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks

Azevedo, T., Campbell, A., Romero-Garcia, R., Passamonti, L., Bethlehem, RAI., Liò, P. and Toschi, N., 2020. A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in resting-state functional MRI Data
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Corso, G., Cavalleri, L., Beaini, D., Liò, P. and Velickovic, P., 2020. Principal neighbourhood aggregation for graph nets Advances in Neural Information Processing Systems, v. 2020-December

John, MS., Nagoth, JA., Ramasamy, KP., Ballarini, P., Mozzicafreddo, M., Mancini, A., Telatin, A., Liò, P., Giuli, G., Natalello, A., Miceli, C. and Pucciarelli, S., 2020. Horizontal gene transfer and silver nanoparticles production in a new Marinomonas strain isolated from the Antarctic psychrophilic ciliate Euplotes focardii. Scientific Reports, v. 10
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Lu, X., Liao, Y., Lio, P. and Pan, H., 2020. An Asynchronous Federated Learning Mechanism for Edge Network Computing Jisuanji Yanjiu yu Fazhan/Computer Research and Development, v. 57
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Ahamad, MM., Aktar, S., Rashed-Al-Mahfuz, M., Uddin, S., Liò, P., Xu, H., Summers, MA., Quinn, JMW. and Moni, MA., 2020. A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients. Expert Systems with Applications, v. 160
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Norcliffe, A., Bodnar, C., Day, B., Simidjievski, N. and Liò, P., 2020. On second order behaviour in augmented neural ODEs Advances in Neural Information Processing Systems, v. 2020-December

Rollins, CPE., Garrison, JR., Arribas, M., Seyedsalehi, A., Li, Z., Chan, RCK., Yang, J., Wang, D., Liò, P., Yan, C., Yi, Z-H., Cachia, A., Upthegrove, R., Deakin, B., Simons, JS., Murray, GK. and Suckling, J., 2020. Evidence in cortical folding patterns for prenatal predispositions to hallucinations in schizophrenia. Transl Psychiatry, v. 10
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2019 (Published online)

Prokhorov, V., Pilehvar, MT., Kartsaklis, D., Lio, P. and Collier, N., 2019 (Published online). Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces Proceedings of the AAAI Conference on Artificial Intelligence, v. 33
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Müller, T. and Lió, P., 2019 (Published online). Personalisable Clinical Decision Support System. ERCIM News, v. 116

Tangherloni, A., Spolaor, S., Rundo, L., Nobile, MS., Cazzaniga, P., Mauri, G., Liò, P., Merelli, I. and Besozzi, D., 2019 (Published online). GenHap: a novel computational method based on genetic algorithms for haplotype assembly. BMC Bioinformatics, v. 20
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Ascolani, G. and Liò, P., 2019 (Published online). Modeling breast cancer progression to bone: how driver mutation order and metabolism matter. BMC Medical Genomics, v. 12
Doi: http://doi.org/10.1186/s12920-019-0541-4

2019 (Accepted for publication)

Spasov, S., Passamonti, L., Duggento, A., Liò, P. and Toschi, N., 2019 (Accepted for publication). A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease NeuroImage,
Doi: http://doi.org/10.1016/j.neuroimage.2019.01.031

Ercole, A., Deasy, J. and Lio, P., 2019 (Accepted for publication). Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning arXiv,

Maj, C., Azevedo, T., Giansanti, V., Borisov, O., Dimitri, GM., Spasov, S., Alzheimer’s Disease Neuroimaging Initiative, , Lió, P. and Merelli, I., 2019 (Accepted for publication). Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer's Disease. Frontiers in Genetics, v. 10
Doi: http://doi.org/10.3389/fgene.2019.00726

Ganggayah, MD., Taib, NA., Har, YC., Lio, P. and Dhillon, SK., 2019 (Accepted for publication). Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Medical Informatics and Decision Making, v. 19
Doi: http://doi.org/10.1186/s12911-019-0801-4

Jana, W., Lio, P. and Lapkin, A., 2019 (Accepted for publication). Identification of strategic molecules for future circular supply chains using large reaction networks Reaction Chemistry and Engineering,

Deasy, J., Lio, P. and Ercole, A., 2019 (Accepted for publication). Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing arXiv,

2019

Parmar, V. and Lió, P., 2019. Multi-omic network regression: Methodology, tool and case study Studies in Computational Intelligence, v. 813
Doi: http://doi.org/10.1007/978-3-030-05414-4_49

Tangherloni, A., Spolaor, S., Rundo, L., Nobile, MS., Cazzaniga, P., Mauri, G., Liò, P., Merelli, I. and Besozzi, D., 2019. GenHap: a novel computational method based on genetic algorithms for haplotype assembly. BMC Bioinform., v. 20-S

Xiaofeng, L., Fangshuo, J., Xiao, Z., Shengwei, Y., Jing, S. and Lio, P., 2019. ASSCA: API sequence and statistics features combined architecture for malware detection Computer Networks, v. 157
Doi: http://doi.org/10.1016/j.comnet.2019.04.007

Zhu, J., Yang, G. and Lio, P., 2019. Lesion focused super-resolution Progress in Biomedical Optics and Imaging - Proceedings of SPIE, v. 10949
Doi: http://doi.org/10.1117/12.2512576

Azevedo, T., Passamonti, L., Lió, P. and Toschi, N., 2019. A machine learning tool for interpreting differences in cognition using brain features
Doi: http://doi.org/10.1101/558403

Deac, A., Huang, Y-H., Veličković, P., Liò, P. and Tang, J., 2019. Drug-Drug Adverse Effect Prediction with Graph Co-Attention

Di Stefano, A., Scatà, M., Vijayakumar, S., Angione, C., La Corte, A. and Liò, P., 2019. Social dynamics modeling of chrono-nutrition. PLoS Comput Biol, v. 15
Doi: http://doi.org/10.1371/journal.pcbi.1006714

Weber, J., Lio’, P. and Lapkin, A., 2019. Identification of Strategic Molecules for Future Circular Supply Chains Using Large Reaction Networks
Doi: http://doi.org/10.26434/chemrxiv.8488934.v1

Scherer, P., Andres-Terre, H., Lio, P. and Jamnik, M., 2019. Decoupling feature propagation from the design of graph auto-encoders

Vignani, R., Liò, P. and Scali, M., 2019. How to integrate wet lab and bioinformatics procedures for wine DNA admixture analysis and compositional profiling: Case studies and perspectives. PLoS One, v. 14
Doi: http://doi.org/10.1371/journal.pone.0211962

Yang, J., Wang, D., Rollins, C., Leming, M., Liò, P., Suckling, J., Murray, G., Garrison, J. and Cachia, A., 2019. Volumetric Segmentation and Characterisation of the Paracingulate Sulcus on MRI Scans
Doi: 10.1101/859496

Smith, HL., Stevens, A., Minogue, B., Sneddon, S., Shaw, L., Wood, L., Adeniyi, T., Xiao, H., Lio, P., Kimber, SJ. and Brison, DR., 2019. Systems based analysis of human embryos and gene networks involved in cell lineage allocation. BMC Genomics, v. 20
Doi: http://doi.org/10.1186/s12864-019-5558-8

Wang, D., Jamnik, M. and Lio, P., 2019. Unsupervised and interpretable scene discovery with Discrete-Attend-Infer-Repeat

Andrés-Terré, H. and Lió, P., 2019. Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders

Bartoszek, K. and Liò, P., 2019. Modelling trait-dependent speciation with approximate Bayesian computation Acta Physica Polonica B, Proceedings Supplement, v. 12
Doi: http://doi.org/10.5506/APhysPolBSupp.12.25

Singh, V. and Lio', P., 2019. Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction

Cangea, C., Velickovic, P. and Lio, P., 2019. XFlow: Cross-Modal Deep Neural Networks for Audiovisual Classification. IEEE Trans Neural Netw Learn Syst,
Doi: http://doi.org/10.1109/TNNLS.2019.2945992

Pernice, S., Follia, L., Balbo, G., Milanesi, L., Sartini, G., Totis, N., Lió, P., Merelli, I., Cordero, F. and Beccuti, M., 2019. Integrating Petri Nets and Flux Balance Methods in Computational Biology Models: A Methodological and Computational Practice Fundamenta Informaticae, v. 171
Doi: http://doi.org/10.3233/FI-2020-1888

Luzhnica, E., Day, B. and Lio', P., 2019. Clique pooling for graph classification

Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres-Terre, H., Shams, Z., Jamnik, M. and Liò, P., 2019. Variational autoencoders for cancer data integration: design principles and computational practice
Doi: http://doi.org/10.1101/719542

Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M. and Liò, P., 2019. Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice. Front Genet, v. 10
Doi: http://doi.org/10.3389/fgene.2019.01205

Akter, T., Shahriare Satu, M., Khan, MI., Ali, MH., Uddin, S., Lio, P., Quinn, JMW. and Moni, MA., 2019. Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders IEEE Access, v. 7
Doi: http://doi.org/10.1109/ACCESS.2019.2952609

Rana, HK., Akhtar, MR., Ahmed, MB., Liò, P., Quinn, JMW., Huq, F. and Moni, MA., 2019. Genetic effects of welding fumes on the progression of neurodegenerative diseases. Neurotoxicology, v. 71
Doi: http://doi.org/10.1016/j.neuro.2018.12.002

Rana, HK., Akhtar, MR., Islam, MB., Ahmed, MB., Liò, P., Quinn, JMW., Huq, F. and Moni, MA., 2019. Genetic effects of welding fumes on the development of respiratory system diseases. Comput Biol Med, v. 108
Doi: http://doi.org/10.1016/j.compbiomed.2019.04.004

2018 (Accepted for publication)

Benmounah, Z., Meshoul, S., Batouche, M. and Lio, P., 2018 (Accepted for publication). Parallel swarm intelligence strategies for large-scale clustering based on MapReduce with application to epigenetics of aging Applied Soft Computing, v. 69
Doi: http://doi.org/10.1016/j.asoc.2018.04.012

2018

Liberis, E., Velickovic, P., Sormanni, P., Vendruscolo, M. and Liò, P., 2018. Parapred: antibody paratope prediction using convolutional and recurrent neural networks. Bioinformatics, v. 34
Doi: http://doi.org/10.1093/bioinformatics/bty305

Sheehan, C., Day, B. and Liò, P., 2018. Introducing Curvature to the Label Space

Cangea, C., Veličković, P., Jovanović, N., Kipf, T. and Liò, P., 2018. Towards Sparse Hierarchical Graph Classifiers

Vijayakumar, S., Conway, M., Lió, P. and Angione, C., 2018. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Briefings in Bioinformatics, v. 19
Doi: http://doi.org/10.1093/bib/bbx053

Karazija, L., Veličković, P. and Liò, P., 2018. Automatic inference of cross-modal connection topologies for X-CNNs Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10878 LNCS
Doi: http://doi.org/10.1007/978-3-319-92537-0_7

Barsacchi, M., Terre, HA. and Lió, P., 2018. GEESE: Metabolically driven latent space learning for gene expression data
Doi: http://doi.org/10.1101/365643

Haider, S., Yao, CQ., Sabine, VS., Grzadkowski, M., Stimper, V., Starmans, MHW., Wang, J., Nguyen, F., Moon, NC., Lin, X., Drake, C., Crozier, CA., Brookes, CL., van de Velde, CJH., Hasenburg, A., Kieback, DG., Markopoulos, CJ., Dirix, LY., Seynaeve, C., Rea, DW., Kasprzyk, A., Lambin, P., Lio', P., Bartlett, JMS. and Boutros, PC., 2018. Pathway-based subnetworks enable cross-disease biomarker discovery. Nat Commun, v. 9
Doi: http://doi.org/10.1038/s41467-018-07021-3

Karazija, L., Velickovic, P. and Liò, P., 2018. Automatic Inference of Cross-modal Connection Topologies for X-CNNs. CoRR, v. abs/1805.00987

He, P., Nakano, T., Mao, Y., Lio, P., Liu, Q. and Yang, K., 2018. Stochastic Channel Switching of Frequency-Encoded Signals in Molecular Communication Networks IEEE Communications Letters, v. 22
Doi: http://doi.org/10.1109/LCOMM.2017.2768537

Cangea, C., Grauslys, A., Liò, P. and Falciani, F., 2018. Structure-Based Networks for Drug Validation

Dimitri, GM., Agrawal, S., Young, A., Donnelly, J., Liu, X., Smielewski, P., Hutchinson, P., Czosnyka, M., Lio, P. and Haubrich, C., 2018. Simultaneous Transients of Intracranial Pressure and Heart Rate in Traumatic Brain Injury: Methods of Analysis. Acta Neurochirurgica: Supplementum, v. 126
Doi: http://doi.org/10.1007/978-3-319-65798-1_31

Tordini, F., Aldinucci, M., Viviani, P., Merelli, I. and Liò, P., 2018. Scientific Workflows on Clouds with Heterogeneous and Preemptible Instances Advances in Parallel Computing, v. 32
Doi: http://doi.org/10.3233/978-1-61499-843-3-605

Iuliano, A., Occhipinti, A., Angelini, C., De Feis, I. and Liò, P., 2018. Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods. Front Genet, v. 9
Doi: http://doi.org/10.3389/fgene.2018.00206

Mancini, A., Eyassu, F., Conway, M., Occhipinti, A., Liò, P., Angione, C. and Pucciarelli, S., 2018. CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design. BMC Bioinformatics, v. 19
Doi: http://doi.org/10.1186/s12859-018-2422-9

Scatà, M., Di Stefano, A., La Corte, A. and Liò, P., 2018. Quantifying the propagation of distress and mental disorders in social networks. Sci Rep, v. 8
Doi: http://doi.org/10.1038/s41598-018-23260-2

Bardozzo, F., Lió, P. and Tagliaferri, R., 2018. A study on multi-omic oscillations in Escherichia coli metabolic networks. BMC Bioinformatics, v. 19
Doi: http://doi.org/10.1186/s12859-018-2175-5

Xiao, H., Bartoszek, K. and Lio', P., 2018. Multi-omic analysis of signalling factors in inflammatory comorbidities. BMC Bioinformatics, v. 19
Doi: http://doi.org/10.1186/s12859-018-2413-x

Veličković, P., Karazija, L., Lane, ND., Bhattacharya, S., Liberis, E., Liò, P., Chieh, A., Bellahsen, O. and Vegreville, M., 2018. Cross-modal recurrent models for weight objective prediction from multimodal time-series data PervasiveHealth: Pervasive Computing Technologies for Healthcare,
Doi: http://doi.org/10.1145/3240925.3240937

Saggese, I., Bona, E., Conway, M., Favero, F., Ladetto, M., Liò, P., Manzini, G. and Mignone, F., 2018. STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow. BMC Bioinformatics, v. 19
Doi: http://doi.org/10.1186/s12859-018-2174-6

Bartocci, E., Lio, P. and Paoletti, N., 2018. Guest Editors' Introduction to the Special Section on the 14th International Conference on Computational Methods in Systems Biology (CMSB 2016) IEEE/ACM Transactions on Computational Biology and Bioinformatics, v. 15
Doi: http://doi.org/10.1109/TCBB.2018.2816979

Felicetti, L., Femminella, M., Reali, G. and Liò, P., 2018. Applications of molecular communications to medicine: a survey. CoRR, v. abs/1808.04242

He, P., Nakano, T., Mao, Y., Liò, P., Liu, Q. and Yang, K., 2018. Stochastic Channel Switching of Frequency-Encoded Signals in Molecular Communication Networks. IEEE Commun. Lett., v. 22
Doi: http://doi.org/10.1109/LCOMM.2017.2768537

Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A. and Bengio, Y., 2018. Graph attention networks 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings,

2017 (Accepted for publication)

Martins, DP., Barros, M., Pierobon, M., Kandhavelu, M., Lio, P. and Balasubramaniam, S., 2017 (Accepted for publication). Computational Models for Trapping Ebola Virus Using Engineered Bacteria IEEE/ACM Transactions on Computational Biology and Bioinformatics, v. 15
Doi: http://doi.org/10.1109/TCBB.2018.2836430

Athanasiadis, E., Botthof, J., Lio, P. and Cvejic, A., 2017 (Accepted for publication). Single-cell RNA Sequencing uncovers transcriptional states and fate decisions in haematopoiesis Nature Communications,

2017

Dimitri, GM. and Lio, P., 2017. DrugClust: A machine learning approach for drugs side effects prediction Computational Biology and Chemistry,

Peychev, M., Veličković, P. and Liò, P., 2017. Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders

Bianchi, L. and Liò, P., 2017. Opportunities for community awareness platforms in personal genomics and bioinformatics education. Brief Bioinform, v. 18
Doi: http://doi.org/10.1093/bib/bbw078

Tordini, F., Drocco, M., Misale, C., Milanesi, L., Liò, P., Merelli, I., Torquati, M. and Aldinucci, M., 2017. NuChart-II: The road to a fast and scalable tool for Hi-C data analysis International Journal of High Performance Computing Applications, v. 31
Doi: http://doi.org/10.1177/1094342016668567

Brouwer, T. and Lio', P., 2017. Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets

Ascolani, G. and Lió, P., 2017. Modelling the order of driver mutations and metabolic mutations as structures in cancer dynamics

Liberis, E., Veličković, P., Sormanni, P., Vendruscolo, M. and Liò, P., 2017. Paratope Prediction using Convolutional and Recurrent Neural Networks
Doi: http://doi.org/10.1101/185488

Oshota, O., Conway, M., Fookes, M., Schreiber, F., Chaudhuri, R., Yu, L., Morgan, F., Clare, S., Choudhary, J., Thomson, N., Lio, P., Maskell, D., Mastroeni, P. and Grant, AJ., 2017. Transcriptome and proteome analysis of Salmonella enterica serovar Typhimurium systemic infection of wild type and immune-deficient mice PLoS ONE, v. 12
Doi: http://doi.org/10.1371/journal.pone.0181365

Barandalla, M., Shi, H., Xiao, H., Colleoni, S., Galli, C., Lio, P., Trotter, M. and Lazzari, G., 2017. Global gene expression profiling and senescence biomarker analysis of hESC exposed to H2O2 induced non-cytotoxic oxidative stress. Stem Cell Res Ther, v. 8
Doi: http://doi.org/10.1186/s13287-017-0602-6

Brouwer, T., Frellsen, J. and Lió, P., 2017. Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10534 LNAI
Doi: http://doi.org/10.1007/978-3-319-71249-9_31

Prokhorov, V., Pilehvar, MT., Kartsaklis, D., Lió, P. and Collier, N., 2017. Learning Rare Word Representations using Semantic Bridging

Kashaf, SS., Angione, C. and Lió, P., 2017. Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization. BMC Syst Biol, v. 11
Doi: http://doi.org/10.1186/s12918-017-0395-3

Moni, MA. and Lio', P., 2017. Genetic Profiling and Comorbidities of Zika Infection. J Infect Dis, v. 216
Doi: http://doi.org/10.1093/infdis/jix327

Dimitri, GM., Agrawal, S., Young, A., Donnelly, J., Liu, X., Smielewski, P., Hutchinson, P., Czosnyka, M., Lió, P. and Haubrich, C., 2017. A multiplex network approach for the analysis of intracranial pressure and heart rate data in traumatic brain injured patients. Appl Netw Sci, v. 2
Doi: http://doi.org/10.1007/s41109-017-0050-3

Bianchi, L. and Liò, P., 2017. Opportunities for community awareness platforms in personal genomics and bioinformatics education. Briefings in Bioinformatics, v. 18
Doi: http://doi.org/10.1093/bib/bbw078

2016 (Published online)

Reali, G. and Lio, P., 2016 (Published online). Simulation Tools for Molecular Communications IEEE TCSIM Newsletter,

2016 (Accepted for publication)

Veličković, P., Wang, D., Lane, ND. and Liò, P., 2016 (Accepted for publication). X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets SSCI 2016: 1-8,

2016

Castellani, GC., Menichetti, G., Garagnani, P., Giulia Bacalini, M., Pirazzini, C., Franceschi, C., Collino, S., Sala, C., Remondini, D., Giampieri, E., Mosca, E., Bersanelli, M., Vitali, S., Valle, IFD., Liò, P. and Milanesi, L., 2016. Systems medicine of inflammaging. Brief Bioinform, v. 17
Doi: http://doi.org/10.1093/bib/bbv062

Bartocci, E. and Lió, P., 2016. Computational Modeling, Formal Analysis, and Tools for Systems Biology. PLoS Comput Biol, v. 12
Doi: http://doi.org/10.1371/journal.pcbi.1004591

Sansom, C., Castiglione, F. and Lio, P., 2016. Metabolic disorders: how can systems modelling help? Lancet Diabetes Endocrinol, v. 4
Doi: http://doi.org/10.1016/S2213-8587(16)00047-4

Felicetti, L., Femminella, M., Reali, G. and Liò, P., 2016. Applications of molecular communications to medicine: A survey Nano Communication Networks, v. 7
Doi: http://doi.org/10.1016/j.nancom.2015.08.004

Angione, C., Conway, M. and Lió, P., 2016. Multiplex methods provide effective integration of multi-omic data in genome-scale models. BMC Bioinformatics, v. 17 Suppl 4
Doi: http://doi.org/10.1186/s12859-016-0912-1

Nardi, F., Frati, F. and Liò, P., 2016. Animal inference on human mitochondrial diseases. Comput Biol Chem, v. 62
Doi: http://doi.org/10.1016/j.compbiolchem.2016.02.002

Schwarz, E., Izmailov, R., Liò, P. and Meyer-Lindenberg, A., 2016. Protein Interaction Networks Link Schizophrenia Risk Loci to Synaptic Function. Schizophr Bull, v. 42
Doi: http://doi.org/10.1093/schbul/sbw035

Brouwer, T., Frellsen, J. and Lio', P., 2016. Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation CoRR abs/1610.08127 (2016),

Shavit, Y., Merelli, I., Milanesi, L. and Lio', P., 2016. How computer science can help in understanding the 3D genome architecture. Brief Bioinform, v. 17
Doi: http://doi.org/10.1093/bib/bbv085

Conway, M., Angione, C. and Liò, P., 2016. Iterative multi level calibration of metabolic networks Current Bioinformatics, v. 11
Doi: http://doi.org/10.2174/1574893611666151203222505

Scatà, M., Di Stefano, A., Liò, P. and La Corte, A., 2016. The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks. Scientific Reports, v. 6
Doi: http://doi.org/10.1038/srep37105

Veličković, P. and Liò, P., 2016. Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types. Bioinformatics, v. 32
Doi: http://doi.org/10.1093/bioinformatics/btw196

Veličković, P. and Lió, P., 2016. Molecular multiplex network inference using Gaussian mixture hidden Markov models Journal of Complex Networks, v. 4
Doi: http://doi.org/10.1093/comnet/cnv029

Angione, C. and Lió, P., 2016. Erratum: Predictive analytics of environmental adaptability in multi-omic network models. Sci Rep, v. 6
Doi: http://doi.org/10.1038/srep26266

Tordini, F., Aldinucci, M., Milanesi, L., Liò, P. and Merelli, I., 2016. The Genome Conformation As an Integrator of Multi-Omic Data: The Example of Damage Spreading in Cancer. Front Genet, v. 7
Doi: http://doi.org/10.3389/fgene.2016.00194

Scatà, M., Di Stefano, A., La Corte, A., Liò, P., Catania, E., Guardo, E. and Pagano, S., 2016. Combining evolutionary game theory and network theory to analyze human cooperation patterns Chaos, Solitons and Fractals, v. 91
Doi: http://doi.org/10.1016/j.chaos.2016.04.018

Narula, P., Piratla, V., Bansal, A., Azad, S. and Lio, P., 2016. Parameter estimation of tuberculosis transmission model using Ensemble Kalman filter across Indian states and union territories Infection, Disease and Health, v. 21
Doi: http://doi.org/10.1016/j.idh.2016.11.001

Lu, X., Lio, P. and Hui, P., 2016. Distance-Based Opportunistic Mobile Data Offloading. Sensors (Basel), v. 16
Doi: http://doi.org/10.3390/s16060878

Iuliano, A., Occhipinti, A., Angelini, C., De Feis, I. and Lió, P., 2016. Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice. Front Physiol, v. 7
Doi: http://doi.org/10.3389/fphys.2016.00208

Capobianco, E. and Lio, P., 2016. Electronic Health Systems: Golden Mine for Precision Medicine The journal of precision medicine,

Shavit, Y., Walker, BJ. and Lio', P., 2016. Hierarchical block matrices as efficient representations of chromosome topologies and their application for 3C data integration. Bioinformatics, v. 32
Doi: http://doi.org/10.1093/bioinformatics/btv736

2015

Liò, P., Miglino, O., Nicosia, G., Nolfi, S. and Pavone, M., 2015. Advances in artificial life: Synthesis and simulation of living systems: Editorial Artificial Life, v. 21
Doi: http://doi.org/10.1162/ARTL_e_00189

Fondi, M. and Liò, P., 2015. Genome-scale metabolic network reconstruction. Methods Mol Biol, v. 1231
Doi: http://doi.org/10.1007/978-1-4939-1720-4_15

Taffi, M., Taffi, M., Paoletti, N., Liò, P., Pucciarelli, S. and Marini, M., 2015. Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea Ecological Modelling, v. 306
Doi: http://doi.org/10.1016/j.ecolmodel.2014.11.030

Capobianco, E. and Liò, P., 2015. Comorbidity networks: Beyond disease correlations Journal of Complex Networks, v. 3
Doi: http://doi.org/10.1093/comnet/cnu048

Angione, C., Costanza, J., Carapezza, G., Lió, P. and Nicosia, G., 2015. Multi-Target Analysis and Design of Mitochondrial Metabolism. PLoS One, v. 10
Doi: http://doi.org/10.1371/journal.pone.0133825

Fondi, M. and Liò, P., 2015. Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology. Microbiol Res, v. 171
Doi: http://doi.org/10.1016/j.micres.2015.01.003

Haider, S., Lipinszki, Z., Przewloka, MR., Ladak, Y., D'Avino, PP., Kimata, Y., Lio', P. and Glover, DM., 2015. DAPPER: a data-mining resource for protein-protein interactions. BioData Min, v. 8
Doi: http://doi.org/10.1186/s13040-015-0063-3

Merelli, I., Tordini, F., Drocco, M., Aldinucci, M., Liò, P. and Milanesi, L., 2015. Integrating multi-omic features exploiting Chromosome Conformation Capture data. Front Genet, v. 6
Doi: http://doi.org/10.3389/fgene.2015.00040

Angione, C., Costanza, J., Carapezza, G., Lió, P. and Nicosia, G., 2015. Analysis and design of molecular machines Theoretical Computer Science, v. 599
Doi: http://doi.org/10.1016/j.tcs.2015.01.030

Bosi, E., Donati, B., Galardini, M., Brunetti, S., Sagot, M-F., Lió, P., Crescenzi, P., Fani, R. and Fondi, M., 2015. MeDuSa: a multi-draft based scaffolder. Bioinformatics, v. 31
Doi: http://doi.org/10.1093/bioinformatics/btv171

Narula, P., Sihota, P., Azad, S. and Lio, P., 2015. Analyzing seasonality of tuberculosis across Indian states and union territories. J Epidemiol Glob Health, v. 5
Doi: http://doi.org/10.1016/j.jegh.2015.02.004

Di Stefano, A., Scatà, M., La Corte, A., Liò, P., Catania, E., Guardo, E. and Pagano, S., 2015. Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory. PLoS One, v. 10
Doi: http://doi.org/10.1371/journal.pone.0140646

Angione, C. and Lió, P., 2015. Predictive analytics of environmental adaptability in multi-omic network models. Sci Rep, v. 5
Doi: http://doi.org/10.1038/srep15147

Angione, C., Pratanwanich, N. and Lió, P., 2015. A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation. ACS Synth Biol, v. 4
Doi: http://doi.org/10.1021/sb5003407

Liò, P., Miglino, O., Nicosia, G., Nolfi, S. and Pavone, M., 2015. Advances in Artificial Life: Synthesis and Simulation of Living Systems: Editorial. Artif Life, v. 21
Doi: http://doi.org/10.1162/ARTL_e_00189

Smedley, D., Haider, S., Durinck, S., Pandini, L., Provero, P., Allen, J., Arnaiz, O., Awedh, MH., Baldock, R., Barbiera, G., Bardou, P., Beck, T., Blake, A., Bonierbale, M., Brookes, AJ., Bucci, G., Buetti, I., Burge, S., Cabau, C., Carlson, JW., Chelala, C., Chrysostomou, C., Cittaro, D., Collin, O., Cordova, R., Cutts, RJ., Dassi, E., Di Genova, A., Djari, A., Esposito, A., Estrella, H., Eyras, E., Fernandez-Banet, J., Forbes, S., Free, RC., Fujisawa, T., Gadaleta, E., Garcia-Manteiga, JM., Goodstein, D., Gray, K., Guerra-Assunção, JA., Haggarty, B., Han, D-J., Han, BW., Harris, T., Harshbarger, J., Hastings, RK., Hayes, RD., Hoede, C., Hu, S., Hu, Z-L., Hutchins, L., Kan, Z., Kawaji, H., Keliet, A., Kerhornou, A., Kim, S., Kinsella, R., Klopp, C., Kong, L., Lawson, D., Lazarevic, D., Lee, J-H., Letellier, T., Li, C-Y., Lio, P., Liu, C-J., Luo, J., Maass, A., Mariette, J., Maurel, T., Merella, S., Mohamed, AM., Moreews, F., Nabihoudine, I., Ndegwa, N., Noirot, C., Perez-Llamas, C., Primig, M., Quattrone, A., Quesneville, H., Rambaldi, D., Reecy, J., Riba, M., Rosanoff, S., Saddiq, AA., Salas, E., Sallou, O., Shepherd, R., Simon, R., Sperling, L., Spooner, W., Staines, DM., Steinbach, D., Stone, K., Stupka, E., Teague, JW., Dayem Ullah, AZ., Wang, J., Ware, D., Wong-Erasmus, M., Youens-Clark, K., Zadissa, A., Zhang, S-J. and Kasprzyk, A., 2015. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res, v. 43
Doi: http://doi.org/10.1093/nar/gkv350

Moni, MA. and Liò, P., 2015. How to build personalized multi-omics comorbidity profiles. Front Cell Dev Biol, v. 3
Doi: http://doi.org/10.3389/fcell.2015.00028

Ascolani, G., Occhipinti, A. and Liò, P., 2015. Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer. PLoS Comput Biol, v. 11
Doi: http://doi.org/10.1371/journal.pcbi.1004199

Xu, H., Moni, MA. and Liò, P., 2015. Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer. Comput Biol Chem, v. 59 Pt B
Doi: http://doi.org/10.1016/j.compbiolchem.2015.08.010

Fondi, M., Maida, I., Perrin, E., Mellera, A., Mocali, S., Parrilli, E., Tutino, ML., Liò, P. and Fani, R., 2015. Genome-scale metabolic reconstruction and constraint-based modelling of the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125. Environ Microbiol, v. 17
Doi: http://doi.org/10.1111/1462-2920.12513

Moni, MA., Xu, H. and Liò, P., 2015. CytoCom: a Cytoscape app to visualize, query and analyse disease comorbidity networks. Bioinformatics, v. 31
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Narula, P., Azad, S. and Lio, P., 2015. Bayesian Melding Approach to Estimate the Reproduction Number for Tuberculosis Transmission in Indian States and Union Territories. Asia Pac J Public Health, v. 27
Doi: http://doi.org/10.1177/1010539515595068

2014

Fondi, M., Orlandini, V., Perrin, E., Maida, I., Bosi, E., Papaleo, MC., Michaud, L., Lo Giudice, A., de Pascale, D., Tutino, ML., Liò, P. and Fani, R., 2014. Draft genomes of three Antarctic Psychrobacter strains producing antimicrobial compounds against Burkholderia cepacia complex, opportunistic human pathogens. Mar Genomics, v. 13
Doi: http://doi.org/10.1016/j.margen.2013.12.009

Pratanwanich, N. and Lio, P., 2014. Exploring the complexity of pathway-drug relationships using latent Dirichlet allocation Computational Biology and Chemistry,
Doi: http://doi.org/10.1016/j.compbiolchem.2014.08.019

Petrov, V., Balasubramaniam, S., Lale, R., Moltchanov, D., Lio', P. and Koucheryavy, Y., 2014. Forward and Reverse coding for chromosome transfer in bacterial nanonetworks Nano Communication Networks, v. 5
Doi: http://doi.org/10.1016/j.nancom.2014.04.003

Capobianco, E. and Lió, P., 2014. Advances in translational biomedicine from systems approaches. Front Genet, v. 5
Doi: http://doi.org/10.3389/fgene.2014.00273

Moni, MA. and Liò, P., 2014. comoR: a software for disease comorbidity risk assessment. J Clin Bioinforma, v. 4
Doi: http://doi.org/10.1186/2043-9113-4-8

Raju, HB., Englander, Z., Capobianco, E., Tsinoremas, NF. and Lerch, JK., 2014. Identification of potential therapeutic targets in a model of neuropathic pain. Front Genet, v. 5
Doi: http://doi.org/10.3389/fgene.2014.00131

Lu, X., Qu, Z., Lio, P., Hui, P., Li, Q., Lu, P. and Bie, R., 2014. Directional communication with movement prediction in mobile wireless sensor networks Personal and Ubiquitous Computing, v. 18
Doi: http://doi.org/10.1007/s00779-014-0793-0

Shavit, Y., Hamey, FK. and Lio, P., 2014. FisHiCal: an R package for iterative FISH-based calibration of Hi-C data. Bioinformatics, v. 30
Doi: http://doi.org/10.1093/bioinformatics/btu491

Taffi, M., Paoletti, N., Liò, P., Pucciarelli, S. and Marini, M., 2014. Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea Ecological Modelling, v. 306
Doi: http://doi.org/10.1016/j.ecolmodel.2014.11.030

Nardi, F., Liò, P., Carapelli, A. and Frati, F., 2014. MtPAN(3): site-class specific amino acid replacement matrices for mitochondrial proteins of Pancrustacea and Collembola. Mol Phylogenet Evol, v. 75
Doi: http://doi.org/10.1016/j.ympev.2014.02.001

Ascolani, G. and Liò, P., 2014. Modeling TGF-β in early stages of cancer tissue dynamics. PLoS One, v. 9
Doi: http://doi.org/10.1371/journal.pone.0088533

Shavit, Y. and Lio', P., 2014. Combining a wavelet change point and the Bayes factor for analysing chromosomal interaction data. Mol Biosyst, v. 10
Doi: http://doi.org/10.1039/c4mb00142g

Pratanwanich, N. and Lió, P., 2014. Pathway-based Bayesian inference of drug-disease interactions. Mol Biosyst, v. 10
Doi: http://doi.org/10.1039/c4mb00014e

Felicetti, L., Femminella, M., Reali, G. and Liò, P., 2014. A molecular communication system in blood vessels for tumor detection Proceedings of the 1st ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2014,
Doi: http://doi.org/10.1145/2619955.2619978

Nardi, F., Liò, P., Carapelli, A. and Frati, F., 2014. MtPAN3: Site-class specific amino acid replacement matrices for mitochondrial proteins of Pancrustacea and Collembola Molecular Phylogenetics and Evolution, v. 75
Doi: http://doi.org/10.1016/j.ympev.2014.02.001

Lió, P., 2014. Computing longevity: Insights from controls Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8738 LNBI
Doi: http://doi.org/10.1007/978-3-319-10398-3_4

Pratanwanich, N. and Lio, P., 2014. Exploring the complexity of pathway-drug relationships using latent Dirichlet allocation. Comput Biol Chem, v. 53 Pt A
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Azad, S. and Lio, P., 2014. Emerging trends of malaria-dengue geographical coupling in the Southeast Asia region. J Vector Borne Dis, v. 51

Taffi, M., Paoletti, N., Liò, P., Tesei, L., Pucciarelli, S. and Marini, M., 2014. Estimation and Modelling of PCBs Bioaccumulation in the Adriatic Sea Ecosystem

Taffi, M., Paoletti, N., Angione, C., Pucciarelli, S., Marini, M. and Liò, P., 2014. Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis. Front Genet, v. 5
Doi: http://doi.org/10.3389/fgene.2014.00319

Moni, MA. and Liò, P., 2014. Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies. BMC Bioinformatics, v. 15
Doi: http://doi.org/10.1186/1471-2105-15-333

2013 (Published online)

Merelli, I., Liò, P. and Milanesi, L., 2013 (Published online). Describing the genes social networks relying on chromosome conformation capture data EMBnet.journal, v. 19
Doi: 10.14806/ej.19.b.735

2013 (No publication date)

Lio, P., 2013 (No publication date). Physio-Environmental Sensing and Live Modeling interactive Journal of Medical Research (i-JMR), v. 2
Doi: http://doi.org/10.2196/ijmr.2092.

2013

Balasubramaniam, S., Ben-Yehuda, S., Pautot, S., Jesorka, A., Lio', P. and Koucheryavy, Y., 2013. A review of experimental opportunities for molecular communication Nano Communication Networks, v. 4
Doi: http://doi.org/10.1016/j.nancom.2013.02.002

Moni, MA., Liò, P. and Milanesi, L., 2013. Comparing viral (HIV) and bacterial (staphylococcus aureus) infection of the bone tissue BIOINFORMATICS 2013 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms,

Angione, C., Carapezza, G., Costanza, J., Lio, P. and Nicosia, G., 2013. Rational design of organelle compartments in cells EMBnet. journal, v. 18

Liò, P., 2013. Pathways to P4 medicine BIOINFORMATICS 2013 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms,

Di Stefano, A., La Corte, A., Leotta, M., Lió, P. and Scatá, M., 2013. It measures like me: An IoTs algorithm in WSNs based on heuristics behavior and clustering methods Ad Hoc Networks,

Angione, C., Costanza, J., Carapezza, G., Lió, P. and Nicosia, G., 2013. Pareto epsilon-dominance and identifiable solutions for BioCAD modeling Proceedings - Design Automation Conference,
Doi: http://doi.org/10.1145/2463209.2488787

Capobianco, E. and Lio', P., 2013. Comorbidity: a multidimensional approach Trends in Molecular Medicine,

Angione, C., Carapezza, G., Costanza, J., Lio, P. and Nicosia, G., 2013. Pareto Optimality in Organelle Energy Metabolism Analysis. IEEE/ACM Trans Comput Biol Bioinform,

Angione, C., Costanza, J., Carapezza, G., Lió, P. and Nicosia, G., 2013. A design automation framework for computational bioenergetics in biological networks. Mol Biosyst, v. 9
Doi: http://doi.org/10.1039/c3mb25558a

Lu, X., Hui, P. and Lio, P., 2013. Offloading Mobile Data from Cellular Networks Through Peer-to-Peer WiFi Communication: A Subscribe-and-Send Architecture CHINA COMMUNICATIONS, v. 10

Merelli, I., Liò, P. and Milanesi, L., 2013. NuChart: An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations PLoS ONE, v. 8
Doi: http://doi.org/10.1371/journal.pone.0075146

Di Stefano, A., La Corte, A., Leotta, M., Lió, P. and Scatá, M., 2013. It measures like me: An IoTs algorithm in WSNs based on heuristics behavior and clustering methods Ad Hoc Networks, v. 11
Doi: http://doi.org/10.1016/j.adhoc.2013.04.011

Taffi, M., Paoletti, N., Liò, P., Tesei, L., Merelli, E. and Marini, M., 2013. A systems biology and ecology framework for POPs bioaccumulation in marine ecosystems Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8130 LNBI

Castiglione, F., Tieri, P., De Graaf, A., Franceschi, C., Liò, P., Van Ommen, B., Mazzà, C., Tuchel, A., Bernaschi, M., Samson, C., Colombo, T., Castellani, GC., Capri, M., Garagnani, P., Salvioli, S., Nguyen, VA., Bobeldijk-Pastorova, I., Krishnan, S., Cappozzo, A., Sacchetti, M., Morettini, M. and Ernst, M., 2013. The onset of type 2 diabetes: proposal for a multi-scale model. JMIR Res Protoc, v. 2
Doi: http://doi.org/10.2196/resprot.2854

Angione, C., Carapezza, G., Costanza, J., Lio, P. and Nicosia, G., 2013. Multi objective design for bacterial communication networks 2013 IEEE International Conference on Communications Workshops, ICC 2013,
Doi: http://doi.org/10.1109/ICCW.2013.6649345

Moni, MA., Mariani, S., Poli, G., Lio, P. and Vicenzi, E., 2013. Differential impacts of R5 vs. X4 HIV-1 on the transcriptome of primary CD4+ T cells RETROVIROLOGY, v. 10
Doi: 10.1186/1742-4690-10-S1-P114

Jacovella, L. and Lio, P., 2013. Speeding up the transition to collective awareness 2013 IEEE International Conference on Communications Workshops, ICC 2013,
Doi: http://doi.org/10.1109/ICCW.2013.6649232

Angione, C., Carapezza, G., Costanza, J., Lió, P. and Nicosia, G., 2013. Design and strain selection criteria for bacterial communication networks Nano Communication Networks, v. 4
Doi: http://doi.org/10.1016/j.nancom.2013.08.001

Vicenzi, E., Liò, P. and Poli, G., 2013. The puzzling role of CXCR4 in human immunodeficiency virus infection. Theranostics, v. 3
Doi: http://doi.org/10.7150/thno.5392

Castiglione, F., Diaz, V., Gaggioli, A., Lio, P., Mazza, C., Merelli, E., Meskers, CGM., Pappalardo, F. and von Ammon, R., 2013. Physio-Environmental Sensing and Live Modeling JOURNAL OF MEDICAL INTERNET RESEARCH, v. 15
Doi: http://doi.org/10.2196/ijmr.2092

Angione, C., Carapezza, G., Costanza, J., Lió, P. and Nicosia, G., 2013. Pareto optimality in organelle energy metabolism analysis. IEEE/ACM Trans Comput Biol Bioinform, v. 10
Doi: http://doi.org/10.1109/TCBB.2013.95

Balasubramaniam, S. and Lio', P., 2013. Multi-hop conjugation based bacteria nanonetworks. IEEE Trans Nanobioscience, v. 12
Doi: http://doi.org/10.1109/TNB.2013.2239657

Brilli, M., Liò, P., Lacroix, V. and Sagot, M-F., 2013. Short and long-term genome stability analysis of prokaryotic genomes. BMC Genomics, v. 14
Doi: http://doi.org/10.1186/1471-2164-14-309

Lu, X., Lio, P., Hui, P. and Jin, H., 2013. A Location Prediction Algorithm for Mobile Communications Using Directional Antennas INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,
Doi: http://doi.org/10.1155/2013/418606

Shavit, Y. and Lio', P., 2013. CytoHiC: a cytoscape plugin for visual comparison of Hi-C networks. Bioinformatics, v. 29
Doi: http://doi.org/10.1093/bioinformatics/btt120

Xie, S., Lawnizak, AT., Lio, P. and Krishnan, S., 2013. Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain Engineering, v. 05
Doi: 10.4236/eng.2013.510b056

Carapezza, G., Umeton, R., Costanza, J., Angione, C., Stracquadanio, G., Papini, A., Lió, P. and Nicosia, G., 2013. Efficient behavior of photosynthetic organelles via Pareto optimality, identifiability, and sensitivity analysis. ACS Synth Biol, v. 2
Doi: http://doi.org/10.1021/sb300102k

Angione, C., Carapezza, G., Costanza, J., Lió, P. and Nicosia, G., 2013. Design and strain selection criteria for bacterial communication networks Nano Communication Networks,

2012 (No publication date)

Laise, P., Fanelli, D., Lio, P. and Arcangeli, A., 2012 (No publication date). Modeling TGF-β signaling pathway in epithelial-mesenchymal transition AIP Advances, v. Special Topic: Physics of Cancer

2012

Liò, P., Merelli, E. and Paoletti, N., 2012. Disease processes as hybrid dynamical systems EPTCS 92, 2012, pp. 152-166,

Peng, C., Jin, X., Wong, K-C., Shi, M. and Liò, P., 2012. Collective human mobility pattern from taxi trips in urban area. PLoS One, v. 7
Doi: http://doi.org/10.1371/journal.pone.0034487

Massaro, E., Bagnoli, F., Guazzini, A. and Lió, P., 2012. Information dynamics algorithm for detecting communities in networks Communications in Nonlinear Science and Numerical Simulation,

Laise, P., Fanelli, D., Lio, P. and Arcangeli, A., 2012. Modeling TGF-beta signaling pathway in epithelial-mesenchymal transition AIP ADV, v. 2
Doi: http://doi.org/10.1063/1.3697962

Massaro, E., Bagnoli, F., Guazzini, A. and Lió, P., 2012. Information dynamics algorithm for detecting communities in networks Communications in Nonlinear Science and Numerical Simulation, v. 17
Doi: 10.1016/j.cnsns.2012.03.023

Umeton, R., Stracquadanio, G., Papini, A., Costanza, J., Liò, P. and Nicosia, G., 2012. Identification of sensitive enzymes in the photosynthetic carbon metabolism. Adv Exp Med Biol, v. 736
Doi: http://doi.org/10.1007/978-1-4419-7210-1_26

Lu, XF., Towsley, D., Lio, P. and Xiong, Z., 2012. An adaptive directional MAC protocol for ad hoc networks using directional antennas Science China Information Sciences, v. 55
Doi: http://doi.org/10.1007/s11432-012-4550-6

Lu, X., Pan, H. and Lio, P., 2012. High Delivery Performance Opportunistic Routing Scheme for Delay Tolerant Networks CHINA COMMUNICATIONS, v. 9

Paoletti, N., Liò, P., Merelli, E. and Viceconti, M., 2012. Multilevel computational modeling and quantitative analysis of bone remodeling. IEEE/ACM Trans Comput Biol Bioinform, v. 9
Doi: http://doi.org/10.1109/TCBB.2012.51

Xie, S., Lawniczak, AT., Krishnan, S. and Lio, P., 2012. Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification ISRN Computational Mathematics, v. 2012
Doi: 10.5402/2012/197352

Liò, P., Angelini, C., De Feis, I. and Nguyen, V-A., 2012. Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species. PLoS One, v. 7
Doi: http://doi.org/10.1371/journal.pone.0042489

Peng, C., Jin, X., Wong, KC., Shi, M. and Liò, P., 2012. Correction: Collective Human Mobility Pattern from Taxi Trips in Urban Area. PLoS One, v. 7
Doi: http://doi.org/10.1371/annotation/f0d48839-ed4b-4cb2-822a-d449a6b4fa5d

Haider, S., Cordeddu, L., Robinson, E., Movassagh, M., Siggens, L., Vujic, A., Choy, M-K., Goddard, M., Lio, P. and Foo, R., 2012. The landscape of DNA repeat elements in human heart failure. Genome Biol, v. 13
Doi: http://doi.org/10.1186/gb-2012-13-10-r90

Angione, C., Liò, P. and Nicosia, G., 2012. How to Compute with Metabolism in Bacteria? ERCIM News, v. 2012

Costanza, J., Carapezza, G., Angione, C., Lió, P. and Nicosia, G., 2012. Robust design of microbial strains. Bioinformatics, v. 28
Doi: http://doi.org/10.1093/bioinformatics/bts590

Bartocci, E., Liò, P., Merelli, E. and Paoletti, N., 2012. Multiple Verification in Complex Biological Systems: The Bone Remodelling Case Study. Trans. Comp. Sys. Biology, v. 14
Doi: http://doi.org/10.1007/978-3-642-35524-0_3

Liò, P., Paoletti, N., Moni, MA., Atwell, K., Merelli, E. and Viceconti, M., 2012. Modelling osteomyelitis. BMC Bioinformatics, v. 13 Suppl 14
Doi: http://doi.org/10.1186/1471-2105-13-S14-S12

Nazri, A. and Lio, P., 2012. Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context. PLoS One, v. 7
Doi: http://doi.org/10.1371/journal.pone.0028713

Costanza, J., Carapezza, G., Angione, C., Liò, P. and Nicosia, G., 2012. Multi-objective optimisation, sensitivity and robustness analysis in FBA modelling Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 7605 LNBI
Doi: http://doi.org/10.1007/978-3-642-33636-2_9

Lio', P. and Balasubramaniam, S., 2012. Opportunistic routing through conjugation in bacteria communication nanonetwork Nano Communication Networks, v. 3
Doi: http://doi.org/10.1016/j.nancom.2011.10.003

2011

Lu, X., Xin, Y. and Lio, P., 2011. ADMAC: An adaptive directional MAC protocol for mobile ad hoc networks Proceedings - 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology, IC-BNMT 2011,
Doi: http://doi.org/10.1109/ICBNMT.2011.6155982

Schwarz, E., Whitfield, P., Nahnsen, S., Wang, L., Major, H., Leweke, FM., Koethe, D., Lio, P. and Bahn, S., 2011. Alterations of primary fatty acid amides in serum of patients with severe mental illness. Front Biosci (Elite Ed), v. 3
Doi: http://doi.org/10.2741/e246

Gilks, WR., Nye, TMW. and Lio, P., 2011. A Variance-Components Model for Distance-Matrix Phylogenetic Reconstruction STAT APPL GENET MOL, v. 10
Doi: http://doi.org/10.2202/1544-6115.1574

Giampieri, E., Remondini, D., de Oliveira, L., Castellani, G. and Lió, P., 2011. Stochastic analysis of a miRNA-protein toggle switch. Mol Biosyst, v. 7
Doi: http://doi.org/10.1039/c1mb05086a

Lio, P. and Sasitharan Balasubramaniam, SB., 2011. Opportunistic routing through conjugation in bacteria communication nanonetwork Nano Communication Networks, v. 2
Doi: http://doi.org/10.1016/j.nancom.2011.10.003

Balasubramaniam, S., Leibnitz, K., Lio', P., Botvich, D. and Murata, M., 2011. Biological Principles for Future Internet Architecture Design IEEE COMMUN MAG, v. 49

Liò, P., Merelli, E., Paoletti, N. and Viceconti, M., 2011. A combined process algebraic and stochastic approach to bone remodeling Electronic Notes in Theoretical Computer Science, v. 277
Doi: http://doi.org/10.1016/j.entcs.2011.09.034

Kitchovitch, S. and Liò, P., 2011. Community structure in social networks: applications for epidemiological modelling. PLoS One, v. 6
Doi: http://doi.org/10.1371/journal.pone.0022220

Van Der Wath, RC., Van Der Wath, EC. and Lió, P., 2011. Parallel hematopoietic stem cell division rate estimation using an agent-based model on the grid Proceedings - 19th International Euromicro Conference on Parallel, Distributed, and Network-Based Processing, PDP 2011,
Doi: http://doi.org/10.1109/PDP.2011.65

Yoneki, E., Crowcroft, J., Lio', P., Walton, N., Vojnovic, M. and Whitaker, R., 2011. Message from the Workshop on the Future of Social Networking COMPUT COMMUN REV, v. 41
Doi: http://doi.org/10.1145/2002250.2002254

Movassagh, M., Choy, MK., Knowles, DA., Cordeddu, L., Haider, S., Down, T., Siggens, L., Vujic, A., Simeoni, I., Penkett, C., Goddard, M., Lio, P., Bennett, MR. and Foo, RSY., 2011. Distinct epigenomic features in end-stage failing human hearts Circulation, v. 124
Doi: http://doi.org/10.1161/CIRCULATIONAHA.111.040071

Leung, IXY., Chan, S-Y., Hui, P. and Lio', P., 2011. Intra-City Urban Network and Traffic Flow Analysis from GPS Mobility Trace

Song, Y. and Liò, P., 2011. Epileptic EEG detection via a novel pattern recognition framework 5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011,
Doi: http://doi.org/10.1109/icbbe.2011.5780179

Bagnoli, F. and Lio, P., 2011. HOW THE MUTATIONAL-SELECTION INTERPLAY ORGANIZES THE FITNESS LANDSCAPE J NONLINEAR MATH PHY, v. 18
Doi: http://doi.org/10.1142/S1402925111001532

Khoo, WM. and Lió, P., 2011. Unity in diversity: Phylogenetic-inspired techniques for reverse engineering and detection of malware families Proceedings - 1st SysSec Workshop, SysSec 2011,
Doi: http://doi.org/10.1109/SysSec.2011.24

Aldinucci, M., Bracciali, A., Liò, P., Sorathiya, A. and Torquati, M., 2011. StochKit-FF: Efficient systems biology on multicore architectures Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6586 LNCS
Doi: http://doi.org/10.1007/978-3-642-21878-1_21

Lio, P. and Verma, D., 2011. Biologically inspired networking and sensing: Algorithms and architectures Biologically Inspired Networking and Sensing: Algorithms and Architectures,
Doi: http://doi.org/10.4018/978-1-61350-092-7

Lio, P. and Verma, D., 2011. Preface Biologically Inspired Networking and Sensing: Algorithms and Architectures,
Doi: http://doi.org/10.4018/978-1-61350-092-7

Umeton, R., Stracquadanio, G., Sorathiya, A., Papini, A., Liò, P. and Nicosia, G., 2011. Design of robust metabolic pathways Proceedings - Design Automation Conference,
Doi: http://doi.org/10.1145/2024724.2024892

Movassagh, M., Choy, M-K., Knowles, DA., Cordeddu, L., Haider, S., Down, T., Siggens, L., Vujic, A., Simeoni, I., Penkett, C., Goddard, M., Lio, P., Bennett, MR. and Foo, RS-Y., 2011. Distinct epigenomic features in end-stage failing human hearts. Circulation, v. 124
Doi: http://doi.org/10.1161/CIRCULATIONAHA.111.040071

Lio, P., Emanuela Merelli, , Nicola Paoletti, NP. and Marco Viceconti, MV., 2011. A Combined Process Algebraic and Stochastic Approach to Bone Remodeling Electronic Notes in Theoretical Computer Science, v. 277

Paoletti, N., Liò, P., Merelli, E. and Viceconti, M., 2011. Osteoporosis: A multiscale modeling viewpoint Proceedings of the 9th International Conference on Computational Methods in Systems Biology, CMSB'11,
Doi: http://doi.org/10.1145/2037509.2037536

Lu, X., Hui, P. and Lio, P., 2011. Evolving model of opportunistic routing in delay tolerant networks Proceedings - 2011 7th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2011,
Doi: http://doi.org/10.1109/MSN.2011.35

Hebenstreit, D., Gu, M., Haider, S., Turner, DJ., Liò, P. and Teichmann, SA., 2011. EpiChIP: gene-by-gene quantification of epigenetic modification levels. Nucleic Acids Res, v. 39
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Balocco, C. and Lio, P., 2011. Assessing ventilation system performance in isolation rooms ENERG BUILDINGS, v. 43
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2010

Lio, P. and Verma, D., 2010. Guest Editorial: Biologically inspired networking IEEE Network, v. 24
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Lu, XF., Wicker, FD., Towsley, D., Xiong, Z. and Lio, P., 2010. Detection Probability Estimation of Directional Antennas and Omni-Directional Antennas WIRELESS PERS COMMUN, v. 55
Doi: http://doi.org/10.1007/s11277-009-9785-1

Angelini, C., De Feis, I., Nguyen, VA., Van Der Wath, R. and Liò, P., 2010. Combining replicates and nearby species data: A Bayesian approach Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6160 LNBI
Doi: http://doi.org/10.1007/978-3-642-14571-1_14

Pappas, V., Verma, DC. and Lio, P., 2010. Morphogenesis in computer networks 33rd IEEE Sarnoff Symposium 2010, Conference Proceedings,
Doi: http://doi.org/10.1109/SARNOF.2010.5469776

Lio, P. and Verma, D., 2010. Biologically Inspired Networking IEEE NETWORK, v. 24

Xie, S., Lawniczak, AT. and Liò, P., 2010. Features extraction via wavelet kernel PCA for data classification Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010,
Doi: http://doi.org/10.1109/MLSP.2010.5588766

Sorathiya, A., Bracciali, A. and Liò, P., 2010. An integrated modelling approach for R5-X4 mutation and HAART therapy assessment Swarm Intelligence,

Liò, P. and Verma, DC., 2010. Biologically inspired networking [Guest Editorial]. IEEE Netw., v. 24
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Song, Y., Azad, S. and Lio, P., 2010. A new approach for epileptic seizure detection using extreme learning machine BIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing, Proceedings,

Stracquadanio, G., Umeton, R., Papini, A., Lio, P. and Nicosia, G., 2010. Analysis and optimization of C<inf>3</inf> photosynthetic carbon metabolism 10th IEEE International Conference on Bioinformatics and Bioengineering 2010, BIBE 2010,
Doi: http://doi.org/10.1109/BIBE.2010.17

Aldinucci, M., Bracciali, A. and Lio, P., 2010. Formal Synthetic Immunology Ercim News, v. 82

Cheng, TMK., Lu, Y-E., Guest, PC., Rahmoune, H., Harris, LW., Wang, L., Ma, D., Stelzhammer, V., Umrania, Y., Wayland, MT., Lió, P. and Bahn, S., 2010. Identification of targeted analyte clusters for studies of schizophrenia. Mol Cell Proteomics, v. 9
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Guazzini, A., Lió, P., Bagnoli, F., Passarella, A. and Conti, M., 2010. Cognitive network dynamics in chatlines Procedia Computer Science, v. 1
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Botta, M., Haider, S., Leung, IXY., Lio, P. and Mozziconacci, J., 2010. Intra- and inter-chromosomal interactions correlate with CTCF binding genome wide. Mol Syst Biol, v. 6
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Kitchovitch, S. and Liò, P., 2010. Risk perception and disease spread on social networks Procedia Computer Science, v. 1
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Xie, S., Lawniczak, AT., Song, Y. and Liò, P., 2010. Feature extraction via dynamic PCA for epilepsy diagnosis and epileptic seizure detection Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010,
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Balocco, C. and Lio, P., 2010. Modelling infection spreading control in a Hospital isolation room Journal of Biomedical Science and Engineering, v. 3
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Lio, P., Guazzini, A., Passarella, A. and Conti, M., 2010. Modeling perisaccadic time perception Journal of Biomedical Science and Engineering, v. 3
Doi: http://doi.org/10.4236/jbise.2010.312147

Sorathiya, A., Bracciali, A. and Liò, P., 2010. Formal reasoning on qualitative models of coinfection of HIV and Tuberculosis and HAART therapy. BMC Bioinformatics, v. 11 Suppl 1
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Lio, P. and Song, Y., 2010. A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine Journal of Biomedical Science and Engineering, v. 3
Doi: http://doi.org/10.4236/jbise.2010.36078

Sorathiya, A., Bracciali, A. and Lio, P., 2010. An integrated modelling approach for R5-X4 mutation and HAART therapy assessment SWARM INTELL-US, v. 4
Doi: http://doi.org/10.1007/s11721-010-0046-4

2009 (Published online)

Lio, P., 2009 (Published online). Modeling space and clocks constraints in visual information processing Frontiers in Neuroinformatics, v. 3
Doi: 10.3389/conf.neuro.11.2009.08.137

2009

Carla Balocco, CB., Lio, P. and Luca Sani, , 2009. Simulazione di un sistema di ventilazione per il controllo degli agenti eziologici nei reparti infettivi. Un caso reale CDA CONDIZIONAMENTO DELL'ARIA RISCALDAMENTO REFRIGERAZIONE, v. May 2009

Lu, XF., Towsley, D., Lio, P., Wicker, F. and Xiong, Z., 2009. Minimizing Detection Probability Routing in Ad Hoc Networks Using Directional Antennas EURASIP J WIREL COMM,
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Fondi, M., Emiliani, G., Liò, P., Gribaldo, S. and Fani, R., 2009. The evolution of histidine biosynthesis in archaea: insights into the his genes structure and organization in LUCA. J Mol Evol, v. 69
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Brilli, M., Fondi, M., Lio, P. and Fani, R., 2009. The Origin and Evolution of Nitrogen Fixation Genes ORIGINS LIFE EVOL B, v. 39

Schwarz, E., Leweke, FM., Bahn, S. and Liò, P., 2009. Clinical bioinformatics for complex disorders: a schizophrenia case study. BMC Bioinformatics, v. 10 Suppl 12
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Wilson, A., Laurenti, E., Oser, G., van der Wath, RC., Blanco-Bose, W., Jaworski, M., Offner, S., Dunant, C., Eshkind, L., Bockamp, E., Lio, P., MacDonald, HR. and Trumpp, A., 2009. Hematopoietic Stem Cells Reversibly Switch from Dormancy to Self-Renewal during Homeostasis and Repair (vol 135, pg 1118, 2008) CELL, v. 138
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Bianchi, L. and Lio, P., 2009. La legge e il DNA Le Scienze, Italian Edition Scientific American,

van der Wath, RC., Wilson, A., Laurenti, E., Trumpp, A. and Liò, P., 2009. Estimating dormant and active hematopoietic stem cell kinetics through extensive modeling of bromodeoxyuridine label-retaining cell dynamics. PLoS One, v. 4
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Leung, IXY., Hui, P., Lio, P. and Crowcroft, J., 2009. Towards real-time community detection in large networks PHYS REV E, v. 79
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Lee, U., Magistretti, E., Gerla, M., Bellavista, P., Lio, P. and Lee, KW., 2009. Bio-inspired multi-agent data harvesting in a proactive urban monitoring environment AD HOC NETW, v. 7
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Wilson, A., Laurenti, E., Oser, G., van der Wath, RC., Blanco-Bose, W., Jaworski, M., Offner, S., Dunant, C., Eshkind, L., Bockamp, E., Lio, P., MacDonald, HR. and Trumpp, A., 2009. Hematopoietic Stem Cells Reversibly Switch from Dormancy to Self-Renewal during Homeostasis and Repair (DOI:10.1016/j.cell.2008.10.048) Cell, v. 138
Doi: http://doi.org/10.1016/j.cell.2009.06.020

Cheng, TMK., Lu, YE. and Lió, P., 2009. Identification of structurally important amino acids in proteins by graph-theoretic measures Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics, StReBio '09,
Doi: http://doi.org/10.1145/1562090.1562092

Nguyen, VA., Koukolikova-Nicola, Z., Bagnoli, F. and Lio, P., 2009. Noise and non-linearities in high-throughput data J STAT MECH-THEORY E,
Doi: http://doi.org/10.1088/1742-5468/2009/01/P01014

Milanesi, L., Romano, P., Castellani, G., Remondini, D. and Liò, P., 2009. Trends in modeling Biomedical Complex Systems. BMC Bioinformatics, v. 10 Suppl 12
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Milanesi, L., Romano, P., Castellani, G., Remondini, D. and Lio, P., 2009. Trends in modeling Biomedical Complex Systems BMC BIOINFORMATICS, v. 10
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Chan, SY., Leung, IXY. and Liò, P., 2009. Fast centrality approximation in modular networks International Conference on Information and Knowledge Management, Proceedings,
Doi: http://doi.org/10.1145/1651274.1651282

2008

Wilson, A., Laurenti, E., Oser, G., van der Wath, RC., Blanco-Bose, W., Jaworski, M., Offner, S., Dunant, CF., Eshkind, L., Bockamp, E., Lió, P., Macdonald, HR. and Trumpp, A., 2008. Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair. Cell, v. 135
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Liò, P., Lawniczak, AT., Xie, S. and Xu, J., 2008. Wavelet-domain statistics of packet switching networks near traffic congestion Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5151 LNCS
Doi: http://doi.org/10.1007/978-3-540-92191-2_24

Brilli, M., Mengoni, A., Fondi, M., Bazzicalupo, M., Liò, P. and Fani, R., 2008. Analysis of plasmid genes by phylogenetic profiling and visualization of homology relationships using Blast2Network. BMC Bioinformatics, v. 9
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Liò, P. and Bishop, M., 2008. Modeling sequence evolution. Methods Mol Biol, v. 452
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Bagnoli, F., Guazzini, A. and Liò, P., 2008. Human Heuristics for Autonomous Agents CoRR, v. abs/0801.3048

Kershenbaum, A., Pappas, V., Lee, KW., Lio, P., Sadler, B. and Verma, D., 2008. A biologically-inspired MANET architecture Proceedings of SPIE - The International Society for Optical Engineering, v. 6981
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Cheng, TMK., Lu, Y-E., Vendruscolo, M., Lio', P. and Blundell, TL., 2008. Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms. PLoS Comput Biol, v. 4
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Lu, X., Wicker, F., Leung, I., Liò, P. and Xiong, Z., 2008. A location prediction algorithm for directional communication IWCMC 2008 - International Wireless Communications and Mobile Computing Conference,
Doi: http://doi.org/10.1109/IWCMC.2008.28

Lio, P. and Bishop, M., 2008. Modeling sequence evolution Methods Mol Biol., v. 452

Lu, YE., Lió, P. and Hand, S., 2008. On low dimensional random projections and similarity search International Conference on Information and Knowledge Management, Proceedings,
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Xie, S., Lawniczak, AT. and Lió, P., 2008. Parametric & non-parametric analysis of mean treatment effects of number of packets in transit in data network model Canadian Conference on Electrical and Computer Engineering,
Doi: http://doi.org/10.1109/CCECE.2008.4564900

Brilli, M., Fani, R. and Liò, P., 2008. Current trends in the bioinformatic sequence analysis of metabolic pathways in prokaryotes. Brief Bioinform, v. 9
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Wilson, A., Osee, G., van der Wath, R., Blanco-Bose, W., Laurenti, E., Dunant, C., Lio, P., MacDonald, HR. and Trumpp, A., 2008. Haematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair SWISS MED WKLY, v. 138

Stajano, F., Bianchi, L., Liò, P. and Korff, D., 2008. Forensic genomics: Kin privacy, driftnets and other open questions Proceedings of the ACM Conference on Computer and Communications Security,
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2007

Weston, EM., Friday, AE. and Liò, P., 2007. Biometric evidence that sexual selection has shaped the hominin face. PloS one, v. 2

Chen, F., Archambault, V., Kar, A., Lio, P., D'Avino, PP., Sinka, R., Lilley, K., Laue, ED., Deak, P., Capalbo, L. and Glover, DM., 2007. Multiple protein phosphatases are required for mitosis in Drosophila CURR BIOL, v. 17
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Papetti, C., Lio, P., Ruber, L., Patarnello, T. and Zardoya, R., 2007. Antarctic fish mitochondrial genomes lack ND6 gene J MOL EVOL, v. 65
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Lawniczak, AT., Xie, S., Liò, PP. and Xu, J., 2007. Study of packet traffic fluctuations near phase transition point from free flow to congestion in data network model Canadian Conference on Electrical and Computer Engineering,
Doi: http://doi.org/10.1109/CCECE.2007.93

Weston, EM., Friday, AE. and Liò, P., 2007. Biometric evidence that sexual selection has shaped the hominin face. PLoS One, v. 2
Doi: http://doi.org/10.1371/journal.pone.0000710

Caretta-Cartozo, C., De Los Rios, P., Piazza, F. and Liò, P., 2007. Bottleneck genes and community structure in the cell cycle network of S. pombe PLoS Computational Biology, v. 3
Doi: http://doi.org/10.1371/journal.pcbi.0030103

van der Wath, E., Moutsianas, L., van der Wath, R., Visagie, A., Milanesi, L. and Lio, P., 2007. Grid methodology for identifying co-regulated genes and transcription factor binding sites IEEE T NANOBIOSCI, v. 6
Doi: http://doi.org/10.1109/TNB.2007.897470

Brilli, M., Fani, R. and Lio, P., 2007. MotifScorer: using a compendium of microarrays to identify regulatory motifs BIOINFORMATICS, v. 23
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Bagnoli, F., Liò, P. and Sguanci, L., 2007. Risk perception in epidemic modeling. Phys Rev E Stat Nonlin Soft Matter Phys, v. 76
Doi: http://doi.org/10.1103/PhysRevE.76.061904

Caretta-Cartozo, C., De Los Rios, P., Piazza, F. and Lio, P., 2007. Bottleneck genes and community structure in the cell cycle network of S-pombe PLOS COMPUT BIOL, v. 3
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Liò, P., 2007. Topological and dynamical properties of genetic and social networks PAMM, v. 7
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Bianchi, L. and Lio, P., 2007. Forensic DNA and bioinformatics BRIEF BIOINFORM, v. 8
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2006

Ambesi-Impiombato, A., Bansal, M., Liò, P. and di Bernardo, D., 2006. Computational framework for the prediction of transcription factor binding sites by multiple data integration. BMC Neurosci, v. 7 Suppl 1
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Bagnoli, F., Lio, P. and Sguanci, L., 2006. Modeling viral coevolution: HIV multi-clonal persistence and competition dynamics PHYSICA A, v. 366
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Sguanci, L., Bagnoli, F. and Lio, P., 2006. Mathematical Model of HIV superinfection dynamics and R5 to X4 switch

Fani, R., Brilli, M. and Liò, P., 2006. Inference from proteobacterial operons shows piecewise organization: a reply to Price et al. J Mol Evol, v. 63
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Fani, R., Caramelli, D. and Liò, P., 2006. From prebiotic chemistry to the evolution of man: The First Conference of the S.I.B.E. (Italian Society of Evolutionary Biology) in Florence Rivista di Biologia - Biology Forum, v. 99

Fani, R., Caramelli, D. and Liò, P., 2006. [It happened... From prebiotic chemistry to human evolution. In Florence, the First Congress of S.I.B.E. September, 4-6, 2006]. Riv Biol, v. 99

Nye, TMW., Lio, P. and Gilks, WR., 2006. A novel algorithm and web-based tool for comparing two alternative phylogenetic trees BIOINFORMATICS, v. 22
Doi: http://doi.org/10.1093/bioinformatics/bti720

2005

Fani, R., Brilli, M. and Lio, P., 2005. The origin and evolution of operons: The piecewise building of the proteobacterial histidine operon J MOL EVOL, v. 60
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Piazza, F. and Lio, P., 2005. Statistical analysis of simple repeats in the human genome PHYSICA A, v. 347
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2004

Rustici, G., Mata, J., Kivinen, K., Lio, P., Penkett, CJ., Burns, G., Hayles, J., Brazma, A., Nurse, P. and Bahler, J., 2004. Periodic gene expression program of the fission yeast cell cycle Nat Genet, v. 36

Lio, P. and Goldman, N., 2004. Phylogenomics and bioinformatics of SARS-CoV TRENDS MICROBIOL, v. 12

Rustici, G., Mata, J., Kivinen, K., Lió, P., Penkett, CJ., Burns, G., Hayles, J., Brazma, A., Nurse, P. and Bähler, J., 2004. Periodic gene expression program of the fission yeast cell cycle. Nat Genet, v. 36
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Tadesse, MG., Vannucci, M. and Lio, P., 2004. Identification of DNA regulatory motifs using Bayesian variable selection BIOINFORMATICS, v. 20
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2003

Lio, P., 2003. Statistical bioinformatic methods in microbial genome analysis BIOESSAYS, v. 25
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Lio, P., 2003. Il genoma della Sars Le Scienze Italian Edition of Scientific American, v. June 2003

Liò, P., 2003. Dimensionality and dependence problems in statistical genomics. Brief Bioinform, v. 4
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Lio, P., 2003. Wavelets in bioinformatics and computational biology: state of art and perspectives BIOINFORMATICS, v. 19

2002

Lio, P., 2002. Investigating the relationship between genome structure, composition, and ecology in prokaryotes MOL BIOL EVOL, v. 19

Lio, P. and Goldman, N., 2002. Modeling mitochondrial protein evolution using structural information J MOL EVOL, v. 54
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Lio, P., 2002. Una vita per le proteine Le Scienze Italian Edition of Scientific American, v. February 2002

Skaer, N., Pistillo, D., Gibert, JM., Lio, P., Wulbeck, C. and Simpson, P., 2002. Gene duplication at the achaete-scute complex and morphological complexity of the peripheral nervous system in Diptera TRENDS GENET, v. 18

Liò, P. and Goldman, N., 2002. Modeling mitochondrial protein evolution using structural information. J Mol Evol, v. 54
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2001

Vannucci, M. and Lio, P., 2001. Non-decimated wavelet analysis of biological sequences: applications to protein structure and genomics Sankhyā: The Indian Journal of Statistics, Series B, v. 63b2

Massingham, T., Davies, LJ. and Lio, P., 2001. Analysing gene function after duplication BIOESSAYS, v. 23

Lio, P., 2001. Dal Genoma al Fisioma

Bogani, P., Simoni, A., Lio, P., Germinario, A. and Buiatti, M., 2001. Molecular variation in plant cell populations evolving in vitro in different physiological contexts. Genome, v. 44

Lio, P., 2001. Le nuove sfide della filogenesi molecolare Le Scienze Italian Edition of Scientific American, v. February 2001

Whelan, S., Lio, P. and Goldman, N., 2001. Molecular phylogenetics: state-of-the-art methods for looking into the past TRENDS GENET, v. 17

2000

Lio, P. and Vannucci, M., 2000. Wavelet change-point prediction of transmembrane proteins. Bioinformatics, v. 16
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Lio, P. and Vannucci, M., 2000. Finding pathogenicity islands and gene transfer events in genome data BIOINFORMATICS, v. 16

Thomas, NS., Wilkinson, J., Lio, P., Doull, I., Morton, NE. and Holgate, ST., 2000. Investigation of the genetic factors underlying asthma and atopy in outbred UK populations Revue des Maladies Respiratoires, v. 17

Fani, R., Gallo, R. and Liò, P., 2000. Molecular evolution of nitrogen fixation: the evolutionary history of the nifD, nifK, nifE, and nifN genes. J Mol Evol, v. 51
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Thomas, NS., Wilkinson, J., Lio, P., Doull, I., Morton, NE. and Holgate, ST., 2000. [Genetic factors involved in asthma and atopy. Studies in British families]. Rev Mal Respir, v. 17

Thomas, NS., Wilkinson, J., Lio, P., Doull, I., Morton, NE. and Holgate, ST., 2000. Investigation of the genetic factors underlying asthma and atopy in outbred UK populations REV MAL RESPIR, v. 17

Hagelberg, E., Goldman, N., Lio, P., Whelan, S., Schiefenhovel, W., Clegg, JB. and Bowden, DK., 2000. Evidence for mitochondrial DNA recombination in a human population of island Melanesia: correction P ROY SOC LOND B BIO, v. 267

Lio, P., 2000. Siamo uomini non DNA robot,

1999

Liò, P. and Goldman, N., 1999. Using protein structural information in evolutionary inference: transmembrane proteins. Mol Biol Evol, v. 16
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Mori, E., Liò, P., Daly, S., Damiani, G., Perito, B. and Fani, R., 1999. Molecular nature of RAPD markers from Haemophilus influenzae Rd genome. Res Microbiol, v. 150
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Hagelberg, E., Goldman, N., Lió, P., Whelan, S., Schiefenhövel, W., Clegg, JB. and Bowden, DK., 1999. Evidence for mitochondrial DNA recombination in a human population of island Melanesia. Proc Biol Sci, v. 266
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1998

Liò, P., Goldman, N., Thorne, JL. and Jones3, DT., 1998. PASSML: combining evolutionary inference and protein secondary structure prediction. Bioinformatics, v. 14
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Liò, P., Goldman, N., Thorne, JL. and Jones, DT., 1998. PASSML: Combining evolutionary inference and protein secondary structure prediction Bioinformatics, v. 14
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Liò, P. and Goldman, N., 1998. Review: Models of molecular evolution and phylogeny Genome Research, v. 8
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Liò, P. and Goldman, N., 1998. Models of molecular evolution and phylogeny. Genome Res, v. 8
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Lio, P. and Ruffo, S., 1998. Searching for genomic constraints NUOVO CIMENTO D, v. 20

1997

Lio, P., 1997. Correlation methods for genomic constraints analysis Annals of Human Genetics, v. 61

Lio, P., 1997. Comparison of multipoint analyses for complex inheritance: IDDM and asthma Annals of Human Genetics, v. 61

Dewar, J., Wheatley, A., Wilkinson, J., Holgate, ST., Thomas, NS., Lio, P., Morton, NE. and Hall, IP., 1997. Association of the Gln 27 beta 2-adrenoceptor polymorphism and IgE variability in asthmatic families. Chest, v. 111
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Fani, R., Tamburini, E., Mori, E., Lazcano, A., Liò, P., Barberio, C., Casalone, E., Cavalieri, D., Perito, B. and Polsinelli, M., 1997. Paralogous histidine biosynthetic genes: evolutionary analysis of the Saccharomyces cerevisiae HIS6 and HIS7 genes. Gene, v. 197
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Liò, P. and Morton, NE., 1997. Comparison of parametric and nonparametric methods to map oligogenes by linkage. Proc Natl Acad Sci U S A, v. 94
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Bogani, P., Liò, P., Intrieri, MC. and Buiatti, M., 1997. A physiological and molecular analysis of the genus Nicotiana. Mol Phylogenet Evol, v. 7
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Dewar, JC., Wilkinson, J., Wheatley, A., Thomas, NS., Doull, I., Morton, N., Lio, P., Harvey, JF., Liggett, SB., Holgate, ST. and Hall, IP., 1997. The glutamine 27 beta2-adrenoceptor polymorphism is associated with elevated IgE levels in asthmatic families. J Allergy Clin Immunol, v. 100
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1996

Alifano, P., Fani, R., Liò, P., Lazcano, A., Bazzicalupo, M., Carlomagno, MS. and Bruni, CB., 1996. Histidine biosynthetic pathway and genes: structure, regulation, and evolution. Microbiol Rev, v. 60
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Bogani, P., Simoni, A., Lio', P., Scialpi, A. and Buiatti, M., 1996. Genome flux in tomato cell clones cultured in vitro in different physiological equilibria. II. A RAPD analysis of variability. Genome, v. 39
Doi: http://doi.org/10.1139/g96-107

Alifano, P., Fani, R., Lio, P., Lazcano, A., Bazzicalupo, M., Carlomagno, MS. and Bruni, CB., 1996. Histidine biosynthetic pathway and genes: Structure, regulation, and evolution MICROBIOL REV, v. 60

Liò, P., Politi, A., Buiatti, M. and Ruffo, S., 1996. High statistics block entropy measures of DNA sequences. J Theor Biol, v. 180
Doi: http://doi.org/10.1006/jtbi.1996.0091

Liò, P., Politi, A., Ruffo, S. and Buiatti, M., 1996. Analysis of genomic patchiness of Haemophilus influenzae and Saccharomyces cerevisiae chromosomes. J Theor Biol, v. 183
Doi: http://doi.org/10.1006/jtbi.1996.0235

1995

Bagnoli, F. and Liò, P., 1995. Selection, mutations and codon usage in a bacterial model. J Theor Biol, v. 173
Doi: http://doi.org/10.1006/jtbi.1995.0062

Fani, R., Liò, P. and Lazcano, A., 1995. Molecular evolution of the histidine biosynthetic pathway. J Mol Evol, v. 41
Doi: http://doi.org/10.1007/BF00173156

VICARIO, F., VENDRAMIN, GG., ROSSI, P., LIO, P. and GIANNINI, R., 1995. ALLOZYME, CHLOROPLAST DNA AND RAPD MARKERS FOR DETERMINING GENETIC-RELATIONSHIPS BETWEEN ABIES-ALBA AND THE RELIC POPULATION OF ABIES NEBRODENSIS THEOR APPL GENET, v. 90

1994

Fani, R., Liò, P., Chiarelli, I. and Bazzicalupo, M., 1994. The evolution of the histidine biosynthetic genes in prokaryotes: a common ancestor for the hisA and hisF genes. J Mol Evol, v. 38
Doi: http://doi.org/10.1007/BF00178849

Lió, P., Ruffo, S. and Buiatti, M., 1994. Third codon G + C periodicity as a possible signal for an "internal" selective constraint. J Theor Biol, v. 171
Doi: http://doi.org/10.1006/jtbi.1994.1225

Book chapters

2025

Joshi, CK. and Liò, P., 2025. gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.
Doi: http://doi.org/10.1007/978-1-0716-4079-1_8

2024

Giannini, F., Fioravanti, S., Barbiero, P., Tonda, A., Liò, P. and Di Lavore, E., 2024. Categorical Foundation of Explainable AI: A Unifying Theory
Doi: 10.1007/978-3-031-63800-8_10

2023

De Maria, E., Despeyroux, J., Felty, A., Liò, P., Olarte, C. and Bahrami, A., 2023. Computational logic for biomedicine and neurosciences
Doi: http://doi.org/10.1002/9781394229086.ch6

Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Liò, P., 2023. Concept Distillation in Graph Neural Networks
Doi: http://doi.org/10.1007/978-3-031-44070-0_12

Ciravegna, G., Giannini, F., Barbiero, P., Gori, M., Lio, P., Maggini, M. and Melacci, S., 2023. Chapter 25. Learning Logic Explanations by Neural Networks
Doi: http://doi.org/10.3233/faia230157

Rocheteau, E., Bica, I., Liò, P. and Ercole, A., 2023. Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients
Doi: 10.1007/978-3-031-36938-4_6

Ciravegna, G., Giannini, F., Barbiero, P., Gori, M., Lio, P., Maggini, M. and Melacci, S., 2023. Learning logic explanations by neural networks
Doi: http://doi.org/10.3233/FAIA230157

2022

DE MARIA, E., DESPEYROUX, J., FELTY, A., LIÒ, P., OLARTE, C. and BAHRAMI, A., 2022. Logique calculatoire pour la biomédecine et les neurosciences
Doi: 10.51926/iste.9029.ch6

Vignani, R., Scali, M. and Liò, P., 2022. Molecular markers and genomics for food and beverages characterization
Doi: 10.1007/978-981-16-4318-7_43

Barsacchi, M., Andres-Terré, H. and Lió, P., 2022. Metabolically driven latent space learning for gene expression data
Doi: http://doi.org/10.1142/9781800610941_0005

2021

Vignani, R., Scali, M. and Liò, P., 2021. Molecular Markers and Genomics for Food and Beverages Characterization
Doi: 10.1007/978-981-15-9364-2_43-1

2018

Vijayakumar, S., Conway, M., Lió, P. and Angione, C., 2018. Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives.
Doi: http://doi.org/10.1007/978-1-4939-7528-0_18

2017

Felicetti, LF., Femminella, MF., Lio', LP., Reali, RG. and Lio, P., 2017. Effect of Aging, Disease Versus Health Conditions in the Design of Nano-communications in Blood Vessels
Doi: http://doi.org/10.1007/978-3-319-50688-3_19

2016

Di Stefano, A., La Corte, A., Lió, P. and Scatá, M., 2016. Bio-Inspired ICT for Big Data Management in Healthcare
Doi: http://doi.org/10.1007/978-3-319-23742-8_1

2015

Liu, Z., Tang, L. and Yan, J., 2015. A random early detection based active queue management algorithm in power optical communication network
Doi: http://doi.org/10.1201/b18592-52

2013

Lio, P., Bianchi, L., Nguyen, V. and Kitchovich, S., 2013. Risk Perception, Heuristics and Epidemic Spread

Bansal, A., Azad, S. and Lio, P., 2013. Malaria incidence forecasting and its implication to intervention strategies in South East Asia Region
Doi: http://doi.org/10.1007/978-3-319-00395-5_110

2012

Lio, P. and Verma, D., 2012. Biologically Inspired Networking and Sensing: Algorithms and Architectures Preface

2010

Brilli, M. and Lio, P., 2010. The structural and dynamical properties of biological systems

Lio, P. and Brilli, M., 2010. Transcription factors and gene regulatory networks

Emiliani, G., Fondi, M., Lio, P. and Fani, R., 2010. Evolution of Metabolic Pathways and Evolution of Genomes

Brilli, M., Fani, R. and Lio, P., 2010. Bioinformatics of gene families

2007

Liò, P., Brilli, M. and Fani, R., 2007. Phylogenetics and Computational Biology of Multigene Families
Doi: 10.1007/978-3-540-35306-5_9

2006 (Published online)

Li��, P. and Bishop, MJ., 2006 (Published online). Nucleic Acid and Protein Sequence Analysis and Bioinformatics
Doi: 10.1002/3527600906.mcb.200400067

2005

Carapelli, A., Nardi, F., Dallai, R., Boore, JL., Lio, P. and Frati, F., 2005. Relationships between hexapods and crustaceans based on 4 mitochondrial genes

Carapelli, A., Nardi, F., Dallai, R., Boore, J., LiÒ, P. and Frati, F., 2005. Relationships between hexapods and crustaceans based on four mitochondrial genes
Doi: 10.1201/9781420037548.ch12

2002

Renato Fani, RF., Silvia Casadei, SC. and Lio, P., 2002. Origin and Evolution of nif Genes
Doi: http://doi.org/10.1007/0-306-47615-0_85

1995

Liò, P., Bazzicalupo, M., Grifoni, A., Mori, E. and Fani, R., 1995. Cloning and Analysis of an Azospirillum brasilense Iteron and hslUV Operon Containing Region
Doi: 10.1007/978-3-642-79906-8_14

Theses / dissertations

2024 (No publication date)

Scherer, P., 2024 (No publication date). Distributional and relational inductive biases for graph representation learning in biomedicine

2023 (No publication date)

Bernstein, A., 2023 (No publication date). Immune Infiltrates in Breast Cancer: Clinical Significance from Histopathology to Prognosis

Zhu, J., 2023 (No publication date). Deep neural networks for medical image super-resolution

Tilly, T., 2023 (No publication date). Deep learning of regulatory sequence variation in Pulmonary Arterial Hypertension

Bodnar, C., 2023 (No publication date). Topological Deep Learning: Graphs, Complexes, Sheaves

Christensen, CN., 2023 (No publication date). Deep learning for image processing in optical super-resolution microscopy

Rocheteau, E., 2023 (No publication date). Representation Learning for Patients in the Intensive Care Unit

2022 (No publication date)

Azevedo, T., 2022 (No publication date). Data-driven Representations in Brain Science: Modelling Approaches in Gene Expression and Neuroimaging Domains

Spivakovsky-Gonzalez, P., 2022 (No publication date). Computational Tools for Metabolic Modeling and Gene Duplication Analysis

Spasov, S., 2022 (No publication date). Encoding parameter and structural efficiency in deep learning

Deasy, J., 2022 (No publication date). Relaxing assumptions in deep probabilistic modelling

2021 (No publication date)

Wang, D., 2021 (No publication date). Neural Diagrammatic Reasoning

Dimanov, B., 2021 (No publication date). Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations

2020 (No publication date)

Andres Terre, H., 2020 (No publication date). Interpreting Deep Learning for cell differentiation. Supervised and Unsupervised models viewed through the lens of information and perturbation theory.

Conference proceedings

2024 (Accepted for publication)

Ceccarelli, F., Holden, SB. and Liò, P., 2024 (Accepted for publication). MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRI

2024

Komorowska, UJ., Mathis, S., Didi, K., Vargas, F., Lio, P. and Jamnik, M., 2024. Dynamics-Informed Protein Design with Structure Conditioning

Bazaga, A., Lio, P. and Micklem, G., 2024. Unsupervised Pretraining for Fact Verification by Language Model Distillation. ICLR,

Papamarkou, T., Birdal, T., Bronstein, M., Carlsson, G., Curry, J., Gao, Y., Hajij, M., Kwitt, R., Liò, P., Di Lorenzo, P., Maroulas, V., Miolane, N., Nasrin, F., Ramamurthy, KN., Rieck, B., Scardapane, S., Schaub, MT., Veličković, P., Wang, B., Wang, Y., Wei, GW. and Zamzmi, G., 2024. Position: Topological Deep Learning is the New Frontier for Relational Learning Proceedings of Machine Learning Research, v. 235

Huang, K., Cao, W., Ta, H., Xiao, X. and Liò, P., 2024. Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach WWW 2024 - Proceedings of the ACM Web Conference,
Doi: 10.1145/3589334.3645705

Giusti, L., Reu, T., Ceccarelli, F., Bodnar, C. and Liò, P., 2024. Topological Message Passing for Higher - Order and Long - Range Interactions Proceedings of the International Joint Conference on Neural Networks,
Doi: 10.1109/IJCNN60899.2024.10650343

Ceccarelli, F., Prinzi, F., Liò, P., Vitabile, S. and Holden, SB., 2024. MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRI Proceedings of the International Joint Conference on Neural Networks,
Doi: http://doi.org/10.1109/IJCNN60899.2024.10650117

Bazaga, A., Lio, P. and Micklem, G., 2024. Language Model Knowledge Distillation for Efficient Question Answering in Spanish. Tiny Papers @ ICLR,

Ceccarelli, F., Giusti, L., Holden, S. and Lio, P., 2024. Integrating Structure and Sequence: Protein Graph Embeddings via GNNs and LLMs
Doi: http://doi.org/10.5220/0012453600003654

Iuliano, A., Lio, P., Manfredi, G. and Romaniello, F., 2024. Denoising Probabilistic Diffusion Models for Synthetic Healthcare Image Generation 2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024 - Proceedings,
Doi: http://doi.org/10.1109/MetroLivEnv60384.2024.10615511

2023

Georgiev, D., Numeroso, D., Bacciu, D. and Lio, P., 2023. Neural Algorithmic Reasoning for Combinatorial Optimisation. LoG, v. 231

Keskin, O., Lupidi, A., Giannini, F., Fioravanti, S., Magister, LC., Barbiero, P. and Liò, P., 2023. Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach Proceedings of Machine Learning Research, v. 221

Lu, X., Zhang, X. and Lio, P., 2023. GAT-DNS: DNS Multivariate Time Series Prediction Model Based on Graph Attention Network ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023,
Doi: http://doi.org/10.1145/3543873.3587329

Borde, HSDO., Kazi, A., Barbero, F. and Liò, P., 2023. Latent Graph Inference using Product Manifolds. ICLR,

Bernárdez, G., Telyatnikov, L., Alarcón, E., Cabellos-Aparicio, A., Barlet-Ros, P. and Liò, P., 2023. Topological Network Traffic Compression GNNet 2023 - Proceedings of the 2nd Graph Neural Networking Workshop 2023,
Doi: 10.1145/3630049.3630172

Mittone, G., Svoboda, F., Aldinucci, M., Lane, N. and Lió, P., 2023. A Federated Learning Benchmark for Drug-Target Interaction ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023,
Doi: http://doi.org/10.1145/3543873.3587687

Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and van der Schaar, M., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis Proceedings of Machine Learning Research, v. 206

Azzolin, S., Longa, A., Barbiero, P., Liò, P. and Passerini, A., 2023. Global Explainability of GNNs via Logic Combination of Learned Concepts. ICLR,

Sun, Z., Cristea, AI., Lio, P. and Yu, J., 2023. Adaptive Distance Message Passing From the Multi-Relational Edge View. Tiny Papers @ ICLR,

Bi, X., Tang, S., Yang, Z., Deng, X., Xiao, B. and Lio, P., 2023. MMCTNet: Multi-Modal Cony-Transformer Network for Predicting Good and Poor Outcomes in Cardiac Arrest Patients Computing in Cardiology,
Doi: http://doi.org/10.22489/CinC.2023.099

Jang, A., Patel, S., Patel, S., Shah, S. and Lio, P., 2023. Predicting mortality in systemic sclerosis patients using machine learning approaches JOURNAL OF INVESTIGATIVE DERMATOLOGY, v. 143

Sun, Z., Harit, A., Cristea, AI., Wang, J. and Lio, P., 2023. A Rewiring Contrastive Patch PerformerMixer Framework for Graph Representation Learning Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023,
Doi: http://doi.org/10.1109/BigData59044.2023.10386951

Di Giovanni, F., Giusti, L., Barbero, F., Luise, G., Liò, P. and Bronstein, M., 2023. On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology Proceedings of Machine Learning Research, v. 202

Barbiero, P., Ciravegna, G., Giannini, F., Zarlenga, ME., Magister, LC., Tonda, A., Lió, P., Precioso, F., Jamnik, M. and Marra, G., 2023. Interpretable Neural-Symbolic Concept Reasoning Proceedings of Machine Learning Research, v. 202

Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and Schaar, MVD., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. AISTATS,

Joshi, CK., Bodnar, C., Mathis, SV., Cohen, T. and Liò, P., 2023. On the Expressive Power of Geometric Graph Neural Networks Proceedings of Machine Learning Research, v. 202

Liu, L., Prost, J., Zhu, L., Papadakis, N., Liò, P., Schönlieb, CB. and Aviles-Rivero, AI., 2023. SCOTCH and SODA: A Transformer Video Shadow Detection Framework Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2023-June
Doi: http://doi.org/10.1109/CVPR52729.2023.01007

Duta, I., Cassarà, G., Silvestri, F. and Lió, P., 2023. Sheaf Hypergraph Networks. NeurIPS,

Opolka, FL., Zhi, YC., Liò, P. and Dong, X., 2023. Graph Classification Gaussian Processes via Spectral Features Proceedings of Machine Learning Research, v. 216

Liu, L., Prost, J., Zhu, L., Papadakis, N., Liò, P., Schönlieb, C-B. and Avilés-Rivero, AI., 2023. SCOTCH and SODA: A Transformer Video Shadow Detection Framework. CVPR,

Zou, X., Zhao, X., Lio, P. and Zhao, Y., 2023. Will More Expressive Graph Neural Networks Do Better on Generative Tasks? LoG, v. 231

2022 (No publication date)

Moss, JD., Opolka, FL., Dumitrascu, B. and Lió, P., 2022 (No publication date). Approximate Latent Force Model Inference

2022 (Accepted for publication)

Margeloiu, A., Simidjievski, N., Lio, P. and Jamnik, M., 2022 (Accepted for publication). Weight predictor network with feature selection for small sample tabular biomedical data

Scherer, P., Lio, P. and Jamnik, M., 2022 (Accepted for publication). Distributed representations of graphs for drug pair scoring Proceedings of the First Learning on Graphs Conference (LoG 2022), v. PMLR 198

Espinosa Zarlenga, M., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Lio, P. and Jamnik, M., 2022 (Accepted for publication). Concept embedding models: Beyond the Accuracy-Explainability Trade-Off

2022

Barbiero, P., Ciravegna, G., Giannini, F., Lió, P., Gori, M. and Melacci, S., 2022. Entropy-Based Logic Explanations of Neural Networks Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, v. 36

Opolka, FL., Zhi, YC., Liò, P. and Dong, X., 2022. Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets Proceedings of Machine Learning Research, v. 151

Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Liò, P., 2022. Graph Neural Networks with Adaptive Readouts. NeurIPS,

Georgiev, D., Barbiero, P., Kazhdan, D., Veličković, P. and Liò, P., 2022. Algorithmic Concept-Based Explainable Reasoning Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, v. 36

Jain, R., Ciravegna, G., Barbiero, P., Giannini, F., Buffelli, D. and Lio, P., 2022. Extending Logic Explained Networks to Text Classification Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022,

Pándy, M., Qiu, W., Corso, G., Veličković, P., Ying, R., Leskovec, J. and Liò, P., 2022. Learning Graph Search Heuristics Proceedings of Machine Learning Research, v. 198

Tailor, SA., Opolka, FL., Liò, P. and Lane, ND., 2022. DO WE NEED ANISOTROPIC GRAPH NEURAL NETWORKS? ICLR 2022 - 10th International Conference on Learning Representations,

Lu, X., Zhao, J. and Lio, P., 2022. Robust android malware detection based on subgraph network and denoising GCN network MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services,
Doi: http://doi.org/10.1145/3498361.3538778

He, Y., Veličković, P., Liò, P. and Deac, A., 2022. Continuous Neural Algorithmic Planners Proceedings of Machine Learning Research, v. 198

Aghakhanyan, G., Barucci, A., Colantonio, S., Colcelli, V., Pasquinelli, F., Gini, R., Lio, P., Mazzei, M., Erba, P., Miele, V. and Neri, E., 2022. NAVIGATOR: An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, v. 49

Cardozo, S., Montero, GI., Kazhdan, D., Dimanov, B., Wijaya, MA., Jamnik, M. and Liò, P., 2022. Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations. CIKM Workshops, v. 3318

Imrie, F., Norcliffe, A., Liò, P. and van der Schaar, M., 2022. Composite Feature Selection Using Deep Ensembles Advances in Neural Information Processing Systems, v. 35

Buffelli, D., Liò, P. and Vandin, F., 2022. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks Advances in Neural Information Processing Systems, v. 35

Lu, X., Pang, R. and Lio, P., 2022. Poster: CFMAP: A Robust CPU Clock Fingerprint Model for Device Authentication Proceedings of the ACM Conference on Computer and Communications Security,
Doi: http://doi.org/10.1145/3548606.3563528

Bodnar, C., Di Giovanni, F., Chamberlain, BP., Liò, P. and Bronstein, M., 2022. Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs Advances in Neural Information Processing Systems, v. 35

Jamasb, AR., Viñas, R., Ma, EJ., Harris, C., Huang, K., Hall, D., Lió, P. and Blundell, TL., 2022. Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks Advances in Neural Information Processing Systems, v. 35

Barbero, F., Bodnar, C., de Ocáriz Borde, HS., Bronstein, M., Veličković, P. and Liò, P., 2022. SH EA F NEU RA L NETWO RK S W ITH CO NN ECTIO N LAPLACIANS Proceedings of Machine Learning Research, v. 196

Buffelli, D., Lió, P. and Vandin, F., 2022. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. NeurIPS,

Imrie, F., Norcliffe, A., Lió, P. and Schaar, MVD., 2022. Composite Feature Selection Using Deep Ensembles. NeurIPS,

Campbell, A., Qendro, L., Liò, P. and Mascolo, C., 2022. ROBUST AND EFFICIENT UNCERTAINTY AWARE BIOSIGNAL CLASSIFICATION VIA EARLY EXIT ENSEMBLES ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 2022-May
Doi: http://doi.org/10.1109/ICASSP43922.2022.9746330

Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Liò, P., 2022. Graph Neural Networks with Adaptive Readouts Advances in Neural Information Processing Systems, v. 35

Zhou, B., Liu, X., Liu, Y., Huang, Y., Liò, P. and Wang, YG., 2022. Well-conditioned Spectral Transforms for Dynamic Graph Representation Proceedings of Machine Learning Research, v. 198

Tilly, T., Auckland, K., Nibhani, R., Martin, J., Nihr, N., Morrell, NW., Lio', P. and Graf, S., 2022. Deep learning of regulatory regions discovers enhancer variants implicated in PAH EUROPEAN RESPIRATORY JOURNAL, v. 60
Doi: http://doi.org/10.1183/13993003.congress-2022.2543

Yi, K., Chen, J., Wang, YG., Zhou, B., Liò, P., Fan, Y. and Hamann, J., 2022. APPROXIMATE EQUIVARIANCE SO(3) NEEDLET CONVOLUTION Proceedings of Machine Learning Research, v. 196

Liu, L., Huang, Z., Liò, P., Schönlieb, CB. and Aviles-Rivero, AI., 2022. You only Look at Patches: A Patch-wise Framework for 3D Unsupervised Medical Image Registration Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13386 LNCS
Doi: http://doi.org/10.1007/978-3-031-11203-4_21

Bodnar, C., Giovanni, FD., Chamberlain, BP., Lió, P. and Bronstein, MM., 2022. Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. NeurIPS,

Opolka, FL., Zhi, Y-C., Liò, P. and Dong, X., 2022. Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets. AISTATS, v. 151

Cardozo, S., Montero, GI., Kazhdan, D., Dimanov, B., Wijaya, M., Jamnik, M. and Lio, P., 2022. Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations CEUR Workshop Proceedings, v. 3318

Day, B., Viñas, R., Simidjievski, N. and Liò, P., 2022. Attentional Meta-learners for Few-shot Polythetic Classification Proceedings of Machine Learning Research, v. 162

Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S. and Liò, P., 2022. 3D Infomax improves GNNs for Molecular Property Prediction Proceedings of Machine Learning Research, v. 162

Fan, J., Pei, J., Bi, X., Xiao, B. and Lio, P., 2022. Context Correlation Aware Network for Cardiac Segmentation Proceedings - IEEE International Conference on Multimedia and Expo, v. 2022-July
Doi: http://doi.org/10.1109/ICME52920.2022.9859985

Lu, X. and Lio, P., 2022. Second International Workshop On Artificial Intelligence To Security - AITS 2022 Proceedings - 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop Volume, DSN-W 2022,
Doi: http://doi.org/10.1109/DSN-W54100.2022.00010

Manouchehrinia, A., Ebrahimi, A., Wiil, UK., Kiani, NA., Lio, P., Olsson, T. and Kockum, I., 2022. A susceptibility network analysis of disease pathways leading to multiple sclerosis MULTIPLE SCLEROSIS JOURNAL, v. 28

Qian, P., Yang, J., Lió, P., Hu, P. and Qi, H., 2022. Joint Group-Wise Motion Estimation and Segmentation of Cardiac Cine MR Images Using Recurrent U-Net Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13413 LNCS
Doi: http://doi.org/10.1007/978-3-031-12053-4_5

Opolka, FL. and Liò, P., 2022. Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes Proceedings of Machine Learning Research, v. 151

2021 (Accepted for publication)

Drotár, P., Jamasb, AR., Day, B., Cangea, C. and Liò, P., 2021 (Accepted for publication). Structure-aware generation of drug-like molecules

Norcliffe, A., Bodnar, C., Day, B., Moss, J. and Liò, P., 2021 (Accepted for publication). Neural ODE Processes

2021

Kazhdan, D., Dimanov, B., Terre, HA., Jamnik, M., Liò, P. and Weller, A., 2021. Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches

Qendro, L., Campbell, A., Liò, P. and Mascolo, C., 2021. Early Exit Ensembles for Uncertainty Quantification Proceedings of Machine Learning Research, v. 158

Rocheteau, E., Liò, P. and Hyland, SL., 2021. Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit. CHIL,

Norcliffe, A., Bodnar, C., Day, B., Moss, J. and Liò, P., 2021. NEURAL ODE PROCESSES ICLR 2021 - 9th International Conference on Learning Representations,

Zhu, J., Tan, C., Yang, J., Yang, G. and Lio', P., 2021. Arbitrary Scale Super-Resolution for Medical Images International Journal of Neural Systems, v. 31
Doi: http://doi.org/10.1142/S0129065721500374

Zubic, N. and Liò, P., 2021. An Effective Loss Function for Generating 3D Models from Single 2D Image Without Rendering. AIAI, v. 627

Sebenius, I., Campbell, A., Morgan, SE., Bullmore, ET. and Lio, P., 2021. Multimodal Graph Coarsening for Interpretable, MRI-Based Brain Graph Neural Network IEEE International Workshop on Machine Learning for Signal Processing, MLSP, v. 2021-January
Doi: http://doi.org/10.1109/MLSP52302.2021.9690626

Wei, X., Pu, C., He, Z. and Lio, P., 2021. Deep Reinforcement Learning-based Vaccine Distribution Strategies Proceedings - 2021 2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021,
Doi: http://doi.org/10.1109/CECIT53797.2021.00082

Bodnar, C., Frasca, F., Wang, YG., Otter, N., Montúfar, G., Liò, P. and Bronstein, MM., 2021. Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Proceedings of Machine Learning Research, v. 139

Lu, X. and Lio, P., 2021. International Workshop on Application of Intelligent Technology in Security - AITS 2021 Proceedings - 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2021,
Doi: http://doi.org/10.1109/DSN-W52860.2021.00008

Zheng, X., Zhou, B., Gao, J., Wang, YG., Liò, P., Li, M. and Montúfar, G., 2021. How Framelets Enhance Graph Neural Networks Proceedings of Machine Learning Research, v. 139

Bodnar, C., Frasca, F., Otter, N., Wang, YG., Lio, P., Montufar, G. and Bronstein, M., 2021. Weisfeiler and Lehman Go Cellular: CW Networks ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021),

Beaini, D., Passaro, S., Létourneau, V., Hamilton, WL., Corso, G. and Liò, P., 2021. Directional Graph Networks Proceedings of Machine Learning Research, v. 139

Corso, G., Ying, R., Pandy, M., Veličković, P., Leskovec, J. and Lio, P., 2021. Neural Distance Embeddings for Biological Sequences Advances in Neural Information Processing Systems, v. 34

Bellini, E., Bagnoli, F., Caporuscio, M., Damiani, E., Flammini, F., Linkov, I., Lio, P. and Marrone, S., 2021. Resilience learning through self adaptation in digital twins of human-cyber-physical systems Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience, CSR 2021,
Doi: http://doi.org/10.1109/CSR51186.2021.9527913

2020 (Published online)

Dmitry, K., Shams, Z. and Pietro, L., 2020 (Published online). MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library. 2020 International Joint Conference on Neural Networks (IJCNN),
Doi: http://doi.org/10.1109/IJCNN48605.2020.9207564

2020 (No publication date)

Bardozzo, F., Lio', P. and Tagliaferri, R., 2020 (No publication date). A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways

Deasy, J., Ercole, A. and Liò, P., 2020 (No publication date). Adaptive Prediction Timing for Electronic Health Records

2020 (Accepted for publication)

Kazhdan, D., Dimanov, B., Jamnik, M., Lio, P. and Weller, A., 2020 (Accepted for publication). Now You See Me (CME): Concept-based Model Extraction

2020

Corso, G., Cavalleri, L., Beaini, D., Liò, P. and Velickovic, P., 2020. Principal Neighbourhood Aggregation for Graph Nets. NeurIPS,

Ma, Z., Xuan, J., Wang, YG., Li, M. and Liò, P., 2020. Path Integral Based Convolution and Pooling for Graph Neural Networks. NeurIPS,

Wang, D., Jamnik, M. and Lio, P., 2020. Abstract Diagrammatic Reasoning with Multiplex Graph Networks

D’Agostino, D., Liò, P., Aldinucci, M. and Merelli, I., 2020. NeoHiC: A web application for the analysis of Hi-C data Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12313 LNBI
Doi: http://doi.org/10.1007/978-3-030-63061-4_10

Kusztos, R., Dimitri, GM. and Lió, P., 2020. Neural Models for Brain Networks Connectivity Analysis Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11925 LNBI
Doi: http://doi.org/10.1007/978-3-030-34585-3_19

Bodnar, C., Day, B. and Lió, P., 2020. Proximal Distilled Evolutionary Reinforcement Learning. AAAI,

Di Stefano, A., Maesa, DDF., Das, SK. and Liò, P., 2020. Resolution of Blockchain Conflicts through Heuristics-based Game Theory and Multilayer Network Modeling. ICDCN 2020: Proceedings of the 21st International Conference on Distributed Computing and Networking,
Doi: http://doi.org/10.1145/3369740.3372914

Dimitri, GM., Beqiri, E., Placek, MM., Czosnyka, M., Ercole, A., Smielewski, P. and Lio, P., 2020. Introducing brain-heart crosstalks information in clinical decision support systems for TBI patients, through ICM+ 2020 11th Conference of the European Study Group on Cardiovascular Oscillations: Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020,
Doi: http://doi.org/10.1109/ESGCO49734.2020.9158050

Dimitri, GM., Spasov, S., Duggento, A., Passamonti, L., Lio, P. and Toschi, N., 2020. Unsupervised stratification in neuroimaging through deep latent embeddings. Annu Int Conf IEEE Eng Med Biol Soc, v. 2020
Doi: http://doi.org/10.1109/EMBC44109.2020.9175810

Azevedo, T., Passamonti, L., Lio, P. and Toschi, N., 2020. A deep spatiotemporal graph learning architecture for brain connectivity analysis. Annu Int Conf IEEE Eng Med Biol Soc, v. 2020
Doi: http://doi.org/10.1109/EMBC44109.2020.9175360

Yeghikyan, G., Opolka, FL., Nanni, M., Lepri, B. and Lio, P., 2020. Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks**To appear in the Proceedings of 2020 IEEE International Conference on Smart Computing (SMARTCOMP 2020) Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020,
Doi: http://doi.org/10.1109/SMARTCOMP50058.2020.00028

Norcliffe, A., Bodnar, C., Day, B., Simidjievski, N. and Lió, P., 2020. On Second Order Behaviour in Augmented Neural ODEs. NeurIPS,

Deasy, J., Simidjievski, N. and Lió, P., 2020. Constraining Variational Inference with Geometric Jensen-Shannon Divergence. NeurIPS,

Filip, A-C., Azevedo, T., Passamonti, L., Toschi, N. and Lio, P., 2020. A novel Graph Attention Network Architecture for modeling multimodal brain connectivity. Annu Int Conf IEEE Eng Med Biol Soc, v. 2020
Doi: http://doi.org/10.1109/EMBC44109.2020.9176613

2019 (No publication date)

Zhu, J., Yang, G. and Lio, P., 2019 (No publication date). How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019),

Cangea, C., Belilovsky, E., Liò, P. and Courville, A., 2019 (No publication date). VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

Taylor, D., Spasov, S. and Liò, P., 2019 (No publication date). Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis and Decision Making

Webb, E., Day, B., Andres-Terre, H. and Lió, P., 2019 (No publication date). Factorised Neural Relational Inference for Multi-Interaction Systems

Opolka, FL., Solomon, A., Cangea, C., Veličković, P., Liò, P. and Hjelm, RD., 2019 (No publication date). Spatio-Temporal Deep Graph Infomax

Veličković, P., Fedus, W., Hamilton, WL., Liò, P., Bengio, Y. and Hjelm, RD., 2019 (No publication date). Deep Graph Infomax

2019 (Accepted for publication)

Azevedo, T., Passamonti, L., Lio, P. and Toschi, N., 2019 (Accepted for publication). A Machine Learning Tool for Interpreting Differences in Cognition Using Brain Features IFIP Advances in Information and Communication Technology,
Doi: http://doi.org/10.1007/978-3-030-19823-7_40

Rossi, E., Monti, F., Bronstein, M. and Liò, P., 2019 (Accepted for publication). ncRNA Classification with Graph Convolutional Networks

2019

Di Stefano, A., Scatà, M., La Corte, A., Das, SK. and Liò, P., 2019. Improving QoE in multi-layer social sensing: A cognitive architecture and game theoretic model SocialSense'19 Proceedings of the Fourth International Workshop on Social Sensing,
Doi: http://doi.org/10.1145/3313294.3313384

Satu, MS., Chandra Howlader, K., Niamat Ullah Akhund, TM., Quinn, JMW., Lio, P. and Moni, MA., 2019. Comorbidity effects of mitochondrial dysfunction to the progression of neurological disorders: Insights from a systems biomedicine perspective 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019,
Doi: http://doi.org/10.1109/ICCIT48885.2019.9038388

Prokhorov, V., Pilehvar, MT., Kartsaklis, D., Liò, P. and Collier, N., 2019. Unseen word representation by aligning heterogeneous lexical semantic spaces 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019,

Spasov, SE. and Liò, P., 2019. Dynamic Neural Network Channel Execution for Efficient Training. BMVC,

Veličković, P., Fedus, W., Hamilton, WL., Bengio, Y., Liò, P. and Devon Hjelm, R., 2019. Deep graph infomax 7th International Conference on Learning Representations, ICLR 2019,

Tangherloni, A., Rundo, L., Spolaor, S., Nobile, MS., Merelli, I., Besozzi, D., Mauri, G., Cazzaniga, P. and Liò, P., 2019. High performance computing for haplotyping: Models and platforms Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11339 LNCS
Doi: http://doi.org/10.1007/978-3-030-10549-5_51

Despeyroux, J., Felty, A., Liò, P. and Olarte, C., 2019. A Logical Framework for Modelling Breast Cancer Progression Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11415 LNCS
Doi: http://doi.org/10.1007/978-3-030-19432-1_8

Serra, A., Guida, MD., Lió, P. and Tagliaferri, R., 2019. Hierarchical block matrix approach for multi-view clustering Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10834 LNBI
Doi: http://doi.org/10.1007/978-3-030-14160-8_19

Cangea, C., Belilovsky, E., Liò, P. and Courville, AC., 2019. VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. ViGIL@NeurIPS,

Zhu, J., Yang, G. and Liò, P., 2019. How Can We Make Gan Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach. ISBI,

Zhu, J., Yang, G. and Lió, P., 2019. Lesion focused super-resolution. Medical Imaging: Image Processing, v. 10949

2018 (No publication date)

Angione, C., Carapezza, G., Costanza, J., Lio, P. and Nicosia, G., 2018 (No publication date). Computing with Metabolic Machines EPiC Series in Computing, v. 10
Doi: 10.29007/t48n

2018 (Accepted for publication)

Wang, D., Jamnik, M. and Lio, P., 2018 (Accepted for publication). Investigating diagrammatic reasoning with deep neural networks
Doi: http://doi.org/10.1007/978-3-319-91376-6_36

Prokhorov, V., Pilehvar, M., Kartsaklis, D., Lio, P. and Collier, N., 2018 (Accepted for publication). Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces

2018

Bica, I., Veličković, P., Xiao, H. and Liò, P., 2018. Multi-omics data integration using cross-modal neural networks ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning,

Mathur, A., Zhang, T., Bhattacharya, S., Velickovic, P., Joffe, L., Lane, ND., Kawsar, F. and Liò, P., 2018. Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices. IPSN '18 Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks,
Doi: http://doi.org/10.1109/IPSN.2018.00048

Velickovic, P., Karazija, L., Lane, ND., Bhattacharya, S., Liberis, E., Liò, P., Chieh, A., Bellahsen, O. and Vegreville, M., 2018. Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data. PervasiveHealth,

Spasov, SE., Passamonti, L., Duggento, A., Lio, P. and Toschi, N., 2018. A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer's Disease. Annu Int Conf IEEE Eng Med Biol Soc, v. 2018
Doi: http://doi.org/10.1109/EMBC.2018.8512468

2018. 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing, PDP 2018, Cambridge, United Kingdom, March 21-23, 2018 PDP,

Merelli, I., Lio, P. and Kotenko, I., 2018. Message from General Chairs Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018,
Doi: http://doi.org/10.1109/PDP2018.2018.00005

Merelli, I., Lio, P. and Kotenko, I., 2018. Message from Organizing Chairs Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018,
Doi: http://doi.org/10.1109/PDP2018.2018.00006

Lu, X., Liang, C., Zhang, S., Lio, P. and Jing, S., 2018. Terminal sensitive data protection by adjusting access time bidirectionally and automatically Proceedings - International Conference on Computer Communications and Networks, ICCCN, v. 2018-July
Doi: http://doi.org/10.1109/ICCCN.2018.8487465

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, RT., Berger, C., Ha, SM., Rozycki, M., Prastawa, M., Alberts, E., Lipkova, J., Freymann, J., Kirby, J., Bilello, M., Fathallah-Shaykh, H., Wiest, R., Kirschke, J., Wiestler, B., Colen, R., Kotrotsou, A., Lamontagne, P., Marcus, D., Milchenko, M., Nazeri, A., Weber, M-A., Mahajan, A., Baid, U., Gerstner, E., Kwon, D., Acharya, G., Agarwal, M., Alam, M., Albiol, A., Albiol, A., Albiol, FJ., Alex, V., Allinson, N., Amorim, PHA., Amrutkar, A., Anand, G., Andermatt, S., Arbel, T., Arbelaez, P., Avery, A., Azmat, M., Pranjal, B., Bai, W., Banerjee, S., Barth, B., Batchelder, T., Batmanghelich, K., Battistella, E., Beers, A., Belyaev, M., Bendszus, M., Benson, E., Bernal, J., Bharath, HN., Biros, G., Bisdas, S., Brown, J., Cabezas, M., Cao, S., Cardoso, JM., Carver, EN., Casamitjana, A., Castillo, LS., Catà, M., Cattin, P., Cerigues, A., Chagas, VS., Chandra, S., Chang, Y-J., Chang, S., Chang, K., Chazalon, J., Chen, S., Chen, W., Chen, JW., Chen, Z., Cheng, K., Choudhury, AR., Chylla, R., Clérigues, A., Colleman, S., Colmeiro, RGR., Combalia, M., Costa, A., Cui, X., Dai, Z., Dai, L., Daza, LA., Deutsch, E., Ding, C., Dong, C., Dong, S., Dudzik, W., Eaton-Rosen, Z., Egan, G., Escudero, G., Estienne, T., Everson, R., Fabrizio, J., Fan, Y., Fang, L., Feng, X., Ferrante, E., Fidon, L., Fischer, M., French, AP., Fridman, N., Fu, H., Fuentes, D., Gao, Y., Gates, E., Gering, D., Gholami, A., Gierke, W., Glocker, B., Gong, M., González-Villá, S., Grosges, T., Guan, Y., Guo, S., Gupta, S., Han, W-S., Han, IS., Harmuth, K., He, H., Hernández-Sabaté, A., Herrmann, E., Himthani, N., Hsu, W., Hsu, C., Hu, X., Hu, X., Hu, Y., Hu, Y., Hua, R., Huang, T-Y., Huang, W., Huffel, SV., Huo, Q., Vivek, HV., Iftekharuddin, KM., Isensee, F., Islam, M., Jackson, AS., Jambawalikar, SR., Jesson, A., Jian, W., Jin, P., Jose, VJM., Jungo, A., Kainz, B., Kamnitsas, K., Kao, P-Y., Karnawat, A., Kellermeier, T., Kermi, A., Keutzer, K., Khadir, MT., Khened, M., Kickingereder, P., Kim, G., King, N., Knapp, H., Knecht, U., Kohli, L., Kong, D., Kong, X., Koppers, S., Kori, A., Krishnamurthi, G., Krivov, E., Kumar, P., Kushibar, K., Lachinov, D., Lambrou, T., Lee, J., Lee, C., Lee, Y., Lee, M., Lefkovits, S., Lefkovits, L., Levitt, J., Li, T., Li, H., Li, W., Li, H., Li, X., Li, Y., Li, H., Li, Z., Li, X., Li, Z., Li, X., Li, W., Lin, Z-S., Lin, F., Lio, P., Liu, C., Liu, B., Liu, X., Liu, M., Liu, J., Liu, L., Llado, X., Lopez, MM., Lorenzo, PR., Lu, Z., Luo, L., Luo, Z., Ma, J., Ma, K., Mackie, T., Madabushi, A., Mahmoudi, I., Maier-Hein, KH., Maji, P., Mammen, CP., Mang, A., Manjunath, BS., Marcinkiewicz, M., McDonagh, S., McKenna, S., McKinley, R., Mehl, M., Mehta, S., Mehta, R., Meier, R., Meinel, C., Merhof, D., Meyer, C., Miller, R., Mitra, S., Moiyadi, A., Molina-Garcia, D., Monteiro, MAB., Mrukwa, G., Myronenko, A., Nalepa, J., Ngo, T., Nie, D., Ning, H., Niu, C., Nuechterlein, NK., Oermann, E., Oliveira, A., Oliveira, DDC., Oliver, A., Osman, AFI., Ou, Y-N., Ourselin, S., Paragios, N., Park, MS., Paschke, B., Pauloski, JG., Pawar, K., Pawlowski, N., Pei, L., Peng, S., Pereira, SM., Perez-Beteta, J., Perez-Garcia, VM., Pezold, S., Pham, B., Phophalia, A., Piella, G., Pillai, GN., Piraud, M., Pisov, M., Popli, A., Pound, MP., Pourreza, R., Prasanna, P., Prkovska, V., Pridmore, TP., Puch, S., Puybareau, É., Qian, B., Qiao, X., Rajchl, M., Rane, S., Rebsamen, M., Ren, H., Ren, X., Revanuru, K., Rezaei, M., Rippel, O., Rivera, LC., Robert, C., Rosen, B., Rueckert, D., Safwan, M., Salem, M., Salvi, J., Sanchez, I., Sánchez, I., Santos, HM., Sartor, E., Schellingerhout, D., Scheufele, K., Scott, MR., Scussel, AA., Sedlar, S., Serrano-Rubio, JP., Shah, NJ., Shah, N., Shaikh, M., Shankar, BU., Shboul, Z., Shen, H., Shen, D., Shen, L., Shen, H., Shenoy, V., Shi, F., Shin, HE., Shu, H., Sima, D., Sinclair, M., Smedby, O., Snyder, JM., Soltaninejad, M., Song, G., Soni, M., Stawiaski, J., Subramanian, S., Sun, L., Sun, R., Sun, J., Sun, K., Sun, Y., Sun, G., Sun, S., Suter, YR., Szilagyi, L., Talbar, S., Tao, D., Tao, D., Teng, Z., Thakur, S., Thakur, MH., Tharakan, S., Tiwari, P., Tochon, G., Tran, T., Tsai, YM., Tseng, K-L., Tuan, TA., Turlapov, V., Tustison, N., Vakalopoulou, M., Valverde, S., Vanguri, R., Vasiliev, E., Ventura, J., Vera, L., Vercauteren, T., Verrastro, CA., Vidyaratne, L., Vilaplana, V., Vivekanandan, A., Wang, G., Wang, Q., Wang, CJ., Wang, W., Wang, D., Wang, R., Wang, Y., Wang, C., Wang, G., Wen, N., Wen, X., Weninger, L., Wick, W., Wu, S., Wu, Q., Wu, Y., Xia, Y., Xu, Y., Xu, X., Xu, P., Yang, T-L., Yang, X., Yang, H-Y., Yang, J., Yang, H., Yang, G., Yao, H., Ye, X., Yin, C., Young-Moxon, B., Yu, J., Yue, X., Zhang, S., Zhang, A., Zhang, K., Zhang, X., Zhang, L., Zhang, X., Zhang, Y., Zhang, L., Zhang, J., Zhang, X., Zhang, T., Zhao, S., Zhao, Y., Zhao, X., Zhao, L., Zheng, Y., Zhong, L., Zhou, C., Zhou, X., Zhou, F., Zhu, H., Zhu, J., Zhuge, Y., Zong, W., Kalpathy-Cramer, J., Farahani, K., Davatzikos, C., Leemput, KV. and Menze, B., 2018. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Wang, D., Zhang, R., Zhu, J., Teng, Z., Huang, Y., Spiga, F., Hong-Fei Du, M., Gillard, JH., Lu, Q. and Liò, P., 2018. Neural network fusion: a novel CT-MR Aortic Aneurysm image segmentation method. Proc SPIE Int Soc Opt Eng, v. 10574
Doi: http://doi.org/10.1117/12.2293371

2017

Heffernan, K., Liò, P. and Teufel, S., 2017. Multilayer data and document stratification for comorbidity analysis Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10477 LNBI
Doi: http://doi.org/10.1007/978-3-319-67834-4_17

Felicetti, L., Femminella, M., Ivanov, T., Lio, P. and Reali, G., 2017. A big-data layered architecture for analyzing molecular communications systems in blood vessels Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication, NanoCom 2017,
Doi: http://doi.org/10.1145/3109453.3109468

Brouwer, T. and Lio, P., 2017. Bayesian Hybrid Matrix Factorisation for Data Integration Proceedings of Machine Learning Research, v. 54

Brouwer, T., Frellsen, J. and Liò, P., 2017. Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation. ECML/PKDD (1), v. 10534
Doi: http://doi.org/10.1007/978-3-319-71249-9_31)

2016

Angione, C., Liò, P., Pucciarelli, S., Can, B., Conway, M., Lotti, M., Bokhari, H., Mancini, A., Sezerman, U. and Telatin, A., 2016. Bioinformatics challenges and potentialities in studying extreme environments Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9874 LNCS
Doi: http://doi.org/10.1007/978-3-319-44332-4_16

Tordini, F., Merelli, I., Liò, P., Milanesi, L. and Aldinucci, M., 2016. NuchaRT: Embedding high-level parallel computing in R for augmented Hi-C data analysis Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9874 LNCS
Doi: http://doi.org/10.1007/978-3-319-44332-4_20

Pratanwanich, N., Lió, P. and Stegle, O., 2016. Warped matrix factorisation for multi-view data integration Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9852 LNAI
Doi: http://doi.org/10.1007/978-3-319-46227-1_49

Alarcon, E., Cid-Fuentes, RG., Davy, A., Felicetti, L., Femminella, M., Lio, P., Reali, G. and Solé-Pareta, J., 2016. MolComML: The molecular communication markup language Proceedings of the 3rd ACM International Conference on Nanoscale Computing and Communication, ACM NANOCOM 2016,
Doi: http://doi.org/10.1145/2967446.2967460

2016. Computational Methods in Systems Biology - 14th International Conference, CMSB 2016, Cambridge, UK, September 21-23, 2016, Proceedings CMSB, v. 9859

Moni, M. and Lio, P., 2016. Infectome, diseasome and comorbidities of Zika infection INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES, v. 53
Doi: http://doi.org/10.1016/j.ijid.2016.11.040

Lu, X. and Lio, P., 2016. Privacy Information Security Classification and Comparison between the Westerner and Chinese Proceedings - 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things, IIKI 2015,
Doi: http://doi.org/10.1109/IIKI.2015.10

He, P., Mao, Y., Liu, Q., Liò, P. and Yang, K., 2016. Channel modelling of molecular communications across blood vessels and nerves 2016 IEEE International Conference on Communications, ICC 2016,
Doi: http://doi.org/10.1109/ICC.2016.7510860

Velickovic, P., Wang, D., Lane, ND. and Liò, P., 2016. X-CNN: Cross-modal convolutional neural networks for sparse datasets. SSCI,

2015

Pratanwanich, N. and Lio, P., 2015. Who wrote this? Textual modeling with authorship attribution in big data IEEE International Conference on Data Mining Workshops, ICDMW, v. 2015-January
Doi: http://doi.org/10.1109/ICDMW.2014.140

Lu, X., Lio, P. and Hui, P., 2015. A content dissemination model for mobile internet to minimize load on cellular network Electronics, Communications and Networks IV - Proceedings of the 4th International Conference on Electronics, Communications and Networks, CECNet2014,
Doi: http://doi.org/10.1201/b18592-54

Tordini, F., Drocco, M., Misale, C., Milanesi, L., Lio, P., Merelli, I. and Aldinucci, M., 2015. Parallel Exploration of the Nuclear Chromosome Conformation with NuChart-II 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP 2015),
Doi: 10.1109/PDP.2015.104

Boutorh, A., Pratanwanich, N., Guessoum, A. and Liò, P., 2015. Drug repurposing by optimizing mining of genes target association Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8623
Doi: http://doi.org/10.1007/978-3-319-24462-4_18

Di Serio, C., Liò, P., Nonis, A. and Tagliaferri, R., 2015. Computational intelligence methods for bioinformatics and biostatistics: 11th international meeting, CIBB 2014 Cambridge, UK, june 26–28, 2014 revised selected papers Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8623

Bardozzo, F., Lió, P. and Tagliaferri, R., 2015. Multi omic oscillations in bacterial pathways Proceedings of the International Joint Conference on Neural Networks, v. 2015-September
Doi: http://doi.org/10.1109/IJCNN.2015.7280853

Tordini, F., Drocco, M., Merelli, I., Milanesi, L., Liò, P. and Aldinucci, M., 2015. NuChart-II: A graph-based approach for analysis and interpretation of Hi-C data Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8623
Doi: http://doi.org/10.1007/978-3-319-24462-4_25

Hamey, FK., Shavit, Y., Maciulyte, V., Town, C., Liò, P. and Tosi, S., 2015. Automated detection of fluorescent probes in molecular imaging Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8623
Doi: http://doi.org/10.1007/978-3-319-24462-4_6

Iuliano, A., Occhipinti, A., Angelini, C., De Feis, I. and Lió, P., 2015. Applications of network-based survival analysis methods for pathways detection in cancer Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8623
Doi: http://doi.org/10.1007/978-3-319-24462-4_7

Korhonen, A., Guo, Y., Baker, S., Yetisgen-Yildiz, M., Stenius, U., Narita, M. and Liò, P., 2015. Improving literature-based discovery with advanced text mining Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8623
Doi: http://doi.org/10.1007/978-3-319-24462-4_8

Tordini, F., Drocco, M., Misale, C., Milanesi, L., Lió, P., Merelli, I. and Aldinucci, M., 2015. Parallel exploration of the nuclear Chromosome Conformation with NuChart-II Proceedings - 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2015,
Doi: http://doi.org/10.1109/PDP.2015.104

2014 (No publication date)

Angione, C., Bartocci, E., Bortolussi, L., Lio, P., Occhipinti, A. and Sanguinetti, G., 2014 (No publication date). Bayesian Design for Whole Cell Synthetic Biology Models Proceedings of the Third International Workshop on Hybrid Systems Biology (HSB 2014),

Angione, C., Pratanwanich, N. and Lio, P., 2014 (No publication date). A hybrid of multi-omics FBA and Bayesian factor modeling to identify pathway crosstalks Proceedings of the 6th International Workshop on Bio-Design Automation (IWBDA),

Scata', M., Di Stefano, A., Giacchi, E., La Corte, A. and Lio, P., 2014 (No publication date). The Bio-Inspired and Social Evolution of Node and Data in a Multilayer Network SCITEPRESS Digital Library,

Fernandes, P., Lio, P. and Milanesi, L., 2014 (No publication date). Challenges in building an e-health infrastructure for P5 Medicine

2014

Lu, X., Lio, P., Hui, P. and Qu, Z., 2014. Nodes density adaptive opportunistic forwarding protocol for intermittently connected networks Proceedings - 2014 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2014,
Doi: http://doi.org/10.1109/IIKI.2014.67

Felicetti, L., Femminella, M., Reali, G. and Liò, P., 2014. Endovascular mobile sensor network for detecting circulating tumoral cells BODYNETS 2014 - 9th International Conference on Body Area Networks,
Doi: http://doi.org/10.4108/icst.bodynets.2014.256917

Bartoszek, K. and Lio, P., 2014. A novel algorithm to reconstruct phylogenies using gene sequences and expression data

2013 (No publication date)

Nguyen, VA. and Lio, P., 2013 (No publication date). Filling in the gaps of biological network

2013

Bansal, A., Azad, S. and Lio, P., 2013. Malaria Incidence Forecasting and Its Implication to Intervention Proceedings of the European Conference on Complex Systems 2012,

Lio, P., Iacovella, L., Bianchi, L. and Nguyen, V., 2013. Information Filtering and Learning: From Heuristics to Social Eudaimonia Proceedings of the European Conference on Complex Systems 2012,

Angione, C., Carapezza, G., Costanza, J., Lio, P. and Nicosia, G., 2013. The Role of the Genome in the Evolution of the Complexity of Metabolic Machines Proceedings of the European Conference on Complex Systems 2012,

2013. Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013, Sicily, Italy, September 2-6, 2013 ECAL,

Bianchi, L., Fernandes, P. and Lio, P., 2013. Improving collective awareness and education about the privacy and ethical issues connected with the genome technologies The Future of Education, Conference Proceedings 2013,

Liò, P., 2013. Methodologies for Systems Medicine: Time to Join the Forces of Bioengineering and Bioinformatics. BIOINFORMATICS,

Pratanwanich, N. and Lio, P., 2013. Bayesian Inference for Learning Between-Pathway Network: A New Tool for Studying Drug-Disease Interactions HUMAN HEREDITY, v. 76

2012

Kim, H., Khoo, WM. and Lio, P., 2012. Polymorphic Attacks against Sequence-based Software Birthmarks

2012. Artificial Immune Systems - 11th International Conference, ICARIS 2012, Taormina, Italy, August 28-31, 2012. Proceedings ICARIS, v. 7597

2011 (No publication date)

Lio, P., 2011 (No publication date). Long Range Properties of DNA Sequences Collana Franco Angeli Editore,

2011

2011. Artificial Immune Systems - 10th International Conference, ICARIS 2011, Cambridge, UK, July 18-21, 2011. Proceedings ICARIS, v. 6825

Lio, P., Emanuela Merelli, and Nicola Paoletti, NP., 2011. Multiple verification in computational modeling of bone pathologies EPTCS, v. 67

Merelli, E., Paoletti, N. and Lio, P., 2011. Methodological Bridges for Multi-Level Systems Procedia Computer Science, v. 7

2010

Chan, TM., Leung, KS., Lee, KH. and Lio, P., 2010. Generic Spaced DNA Motif Discovery Using Genetic Algorithm 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC),

Papini, A., Nicosia, G., Stracquadanio, G., Lio, P. and Umeton, R., 2010. Key Enzymes for the Optimization of CO2 Uptake and Nitrogen Consumption in the C-3 Photosynthetic Carbon Metabolism JOURNAL OF BIOTECHNOLOGY, v. 150
Doi: http://doi.org/10.1016/j.jbiotec.2010.09.846

Ostilli, M., Yoneki, E., Leung, IXY., Mendes, JFF., Lio, P. and Crowcroft, J., 2010. Statistical mechanics of rumour spreading in network communities ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS, v. 1
Doi: http://doi.org/10.1016/j.procs.2010.04.262

Papini, A., Mosti, S., Lio, P. and Haider, S., 2010. BIOLIP, a biotechnology-oriented database of oil content in plants, algae, fungi and cyanobacteria JOURNAL OF BIOTECHNOLOGY, v. 150
Doi: http://doi.org/10.1016/j.jbiotec.2010.09.012

Kitchovitch, S. and Lio, P., 2010. Risk perception and disease spread on social networks ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS, v. 1
Doi: http://doi.org/10.1016/j.procs.2010.04.264

Guazzini, A., Lio, P., Bagnoli, F., Passarella, A. and Conti, M., 2010. Cognitive network dynamics in chatlines ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS, v. 1
Doi: http://doi.org/10.1016/j.procs.2010.04.265

Bartoszek, K., Lio, P. and Sorathiya, A., 2010. INFLUENZA DIFFERENTIATION AND EVOLUTION SUMMER SOLSTICE 2009 INTERNATIONAL CONFERENCE ON DISCRETE MODELS OF COMPLEX SYSTEMS, v. 3

Aldinucci, M., Bracciali, A., Liò, P., Sorathiya, A. and Torquati, M., 2010. StochKit-FF: Efficient Systems Biology on Multicore Architectures. Euro-Par Workshops, v. 6586

2009

Sorathiyar, A., Lio, P. and Sguanci, L., 2009. Mathematical Model of HIV Superinfection and Comparative Drug Therapy ARTIFICIAL IMMUNE SYSTEMS, PROCEEDINGS, v. 5666

Xie, SK., Lio, P. and Lawniczak, AT., 2009. A Case Study of ICA with Multi-scale PCA of Simulated Traffic Data ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT II, v. 5769

Nguyen, VA. and Lio, P., 2009. Measuring similarity between gene expression profiles: a Bayesian approach BMC GENOMICS, v. 10
Doi: http://doi.org/10.1186/1471-2164-10-S3-S14

Lu, Y-E., Roberts, SGB., Cheng, TMK., Dunbar, R., Liò, P. and Crowcroft, J., 2009. On optimising personal network size to manage information flow. CIKM-CNIKM,

Hui, P., Xu, K., Li, VOK., Crowcroft, J., Latora, V. and Lio, P., 2009. Selfishness, Altruism and Message Spreading in Mobile Social Networks IEEE INFOCOM 2009 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS,

Lu, Y-E., Roberts, SGB., Liò, P., Dunbar, R. and Crowcroft, J., 2009. Size Matters: Variation in Personal Network Size, Personality and Effect on Information Transmission. CSE (4),
Doi: 10.1109/CSE.2009.179

Xie, SK., Lio, P. and Lawniczak, AT., 2009. A Comparative Study of Noise Effect on Wavelet Based De-noising Methods IEEE TIC-STH 09: 2009 IEEE TORONTO INTERNATIONAL CONFERENCE: SCIENCE AND TECHNOLOGY FOR HUMANITY,

Xu, K., Hui, P., Li, VOK., Crowcroft, J., Latora, V. and Lio, P., 2009. Impact of Altruism on Opportunistic Communications 2009 FIRST INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS,

Kitchovitch, S., Song, YD., van der Wath, R. and Lio, P., 2009. Substitution Matrices and Mutual Information Approaches to Modeling Evolution LEARNING AND INTELLIGENT OPTIMIZATION, v. 5851

Kitchovitch, S., Leung, I., Song, YD. and Lio, P., 2009. Using Mutual Information and Models of Evolution for improved pattern detection 2009 INTERNATIONAL JOINT CONFERENCE ON BIOINFORMATICS, SYSTEMS BIOLOGY AND INTELLIGENT COMPUTING, PROCEEDINGS,
Doi: http://doi.org/10.1109/IJCBS.2009.77

Bella, G. and Lio, P., 2009. Formal Analysis of the Genetic Toggle COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 5688

Giampaolo Bella, GB. and Lio, P., 2009. Analysing the microRNA-17-92/Myc/E2F/RB Compound Toggle Switch by Theorem Proving Proc. of the 9th Workshop on Network Tools and Applications in Biology (Nettab’09), v. Liberodiscrivere (2009)

Sorathiya, A., Jucikas, T., Piecewicz, S., Sengupta, S. and Lio, P., 2009. Searching for Glycomics Role in Stem Cell Development COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, v. 5488

Guazzini, A., Lio, P., Passarella, A. and Conti, M., 2009. Information Processing and Timing Mechanisms in Vision ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT I, v. 5768

2008

Lu, XF., Hui, P., Lio, P. and Xiong, Z., 2008. Identity Privacy Protection by Delayed Transmission in Pocket Switched Networks EUC 2008: PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING, VOL 2, WORKSHOPS,

Lio, P., Brilli, M. and Fani, R., 2008. Topological metrics in Blast data mining: Plasmid and nitrogen-fixing proteins case studies BIOINFORMATICS RESEARCH AND DEVELOPMENT, PROCEEDINGS, v. 13

Lio, P., Angelini, C., DeFeis, I., Nguyen, V., Cutillo, L. and va der Wath, R., 2008. Statistical issues for combining replicates and nearby species data and different omics Proceedings The Art and Science of Statistical Bioinformatics The 27th Leeds Annual Statistical Research Workshop 15th - 17th July 2008,

Nguyen, VA., Koukolikova-Nicola, Z., Bagnoli, F. and Lio, P., 2008. Bayesian Inference on Hidden Knowledge in High-Throughput Molecular Biology Data PRICAI 2008: TRENDS IN ARTIFICIAL INTELLIGENCE, v. 5351

van der Wath, RC. and Lio, P., 2008. A Stochastic Single Cell Based Model of BrdU Measured Hematopoietic Stem Cell Kinetics COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 5307

Lee, U., Magistretti, E., Gerla, M., Bellavista, P., Lio, P. and Lee, KW., 2008. Bio-Inspired Multi-agent Collaboration for Urban Monitoring Applications BIO-INSPIRED COMPUTING AND COMMUNICATION, v. 5151

van der Wath, RC. and Lio, P., 2008. A Stochastic Multi-agent Model of Stem Cell Proliferation CELLULAR AUTOMATA, PROCEEDINGS, v. 5191

Koukolikova-Nicola, Z., Lio, P. and Bagnoli, F., 2008. Inference on missing values in genetic networks using high-throughput data EVOLUTIONARY COMPUTATION, MACHINE LEARNING AND DATA MINING IN BIOINFORMATICS, PROCEEDINGS, v. 4973

Kershenbaum, A., Pappas, V., Lee, KW., Lio, P., Sadler, B. and Verma, D., 2008. A biologically-inspired MANET architecture - art. no. 698106 DEFENSE TRANSFORMATION AND NET-CENTRIC SYSTEMS 2008, v. 6981

Angelini, C., Cutillo, L., De Feis, I., Lio, P. and van der Wath, R., 2008. Combining experimental evidences from replicates and nearby species data for annotating novel genomes COLLECTIVE DYNAMICS: TOPICS ON COMPETITION AND COOPERATION IN THE BIOSCIENCES, v. 1028

Leung, IXY., Gibbs, G., Bagnoli, F., Sorathiya, A. and Lio, P., 2008. Contact Network Modeling of Flu Epidemics CELLULAR AUTOMATA, PROCEEDINGS, v. 5191

Lu, XF., Chen, YC., Leung, I., Xiong, Z. and Lio, P., 2008. A novel mobility model from a heterogeneous military MANET trace AD-HOC, MOBILE AND WIRELESS NETWORKS, PROCEEDINGS, v. 5198

van der Wath, RC., van der Wath, E., Carapelli, A., Nardi, F., Frati, F., Milanesi, L. and Lio, P., 2008. Bayesian phylogeny on grid BIOINFORMATICS RESEARCH AND DEVELOPMENT, PROCEEDINGS, v. 13

Xie, SK., Lawniczak, AT. and Lio, P., 2008. Parametric & non-parametric analysis of mean treatment effects of number of packets in transit in data network model 2008 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-4,

2008. Bio-Inspired Computing and Communication, First Workshop on Bio-Inspired Design of Networks, BIOWIRE 2007, Cambridge, UK, April 2-5, 2007, Revised Selected Papers BIOWIRE, v. 5151

Lu, XF., Wicker, F., Lio', P. and Towsley, D., 2008. Security Estimation Model with Directional Antennas 2008 IEEE MILITARY COMMUNICATIONS CONFERENCE: MILCOM 2008, VOLS 1-7,

Allen, SM., Conti, M., Crowcroft, J., Dunbar, R., Liò, P., Mendes, JFF., Molva, R., Passarella, A., Stavrakakis, I. and Whitaker, RM., 2008. Social Networking for Pervasive Adaptation. SASO Workshops,

Kershenbaum, A., Pappas, V., Lee, KW., Lio, P., Sadler, B. and Verma, D., 2008. A Biologically-Inspired MANET Architecture Proceedings of SPIE, the International Society for Optical Engineering,

Lu, YE., Lio, P. and Hand, S., 2008. Beta Random Projection BIO-INSPIRED COMPUTING AND COMMUNICATION, v. 5151

Schwarz, E., Leweke, FM., Bahn, S. and Lio, P., 2008. Combining molecular and physiological data of complex disorders BIOINFORMATICS RESEARCH AND DEVELOPMENT, PROCEEDINGS, v. 13

Allen, SM., Conti, M., Crowcroft, J., Dunbar, R., Lio, P., Mendes, JF., Molva, R., Passarella, A., Stavrakakis, I. and Whitaker, RM., 2008. Social Networking for Pervasive Adaptation SASOW 2008: SECOND IEEE INTERNATIONAL CONFERENCE ON SELF-ADAPTIVE AND SELF-ORGANIZING SYSTEMS WORKSHOPS, PROCEEDINGS,

Bagnoli, F., Guazzini, A. and Lio, P., 2008. Human Heuristics for Autonomous Agents BIO-INSPIRED COMPUTING AND COMMUNICATION, v. 5151

2007

Lu, YE., Lio, P. and Hand, S., 2007. Beta random projection ISM WORKSHOPS 2007: NINTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA - WORKSHOPS, PROCEEDINGS,
Doi: http://doi.org/10.1109/ISM.Workshops.2007.61

Sguanci, L., Bagnoli, F. and Lio, P., 2007. Modeling HIV quasispecies evolutionary dynamics BMC EVOLUTIONARY BIOLOGY, v. 7
Doi: http://doi.org/10.1186/1471-2148-7-S2-S5

Milanesi, L., Lio, P. and Breton, V., 2007. Bioinformatics Challenges in Life Science IST-Africa 2007 Conference Proceedings, Paul Cunningham and Miriam Cunningham (Eds), IIMC International Information Management Corporation, 2007, ISBN: 1-905824-04-1,

Angelini, C., Cutillo, L., De Feis, I., Van der Wath, R. and Lio, P., 2007. Identifying regulatory sites using neighborhood species Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedings, v. 4447

Lawniczak, AT., Lio, P., Xie, S. and Xu, JY., 2007. Wavelet spectral analysis of packet traffic near phase transition point from free flow to congestion in data network model 2007 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3,

Fani, R., Brilli, M., Fondi, M. and Lio, P., 2007. The role of gene fusions in the evolution of metabolic pathways: the histidine biosynthesis case BMC EVOLUTIONARY BIOLOGY, v. 7
Doi: http://doi.org/10.1186/1471-2148-7-S2-S4

Carapelli, A., Lio, P., Nardi, F., van der Wath, E. and Frati, F., 2007. Phylogenetic analysis of mitochondrial protein coding genes confirms the reciprocal paraphyly of Hexapoda and Crustacea BMC EVOLUTIONARY BIOLOGY, v. 7
Doi: http://doi.org/10.1186/1471-2148-7-S2-S8

Lu, YE., Hand, S. and Lio, P., 2007. Keyword searching in structured overlays via content distance addressing Databases, Information Systems, and Peer-to-Peer Computing, v. 4125

2006

Sguanci, L., Lio, P. and Bagnoli, F., 2006. The influence of risk perception in epidemics: A cellular agent model CELLULAR AUTOMATA, PROCEEDINGS, v. 4173

Sguanci, L., Lio, P. and Bagnoli, F., 2006. Modeling evolutionary dynamics of HIV infection COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 4210

Fani, R., Caramelli, D. and Lio, P., 2006. It happened... From prebiotic chemistry to human evolution Rivista di biologia,

2005

Lu, YE., Hand, S. and Lio, P., 2005. Keyword searching in hypercubic manifolds Fifth IEEE International Conference on Peer-to-Peer Computing, Proceedings,

Lio, P., 2005. Phylogenetic and structural analysis of mitochondrial complex I proteins GENE, v. 345
Doi: http://doi.org/10.1016/j.gene.2004.11.033

2003

Lio, P. and Vannucci, M., 2003. Investigating the evolution and structure of chemokine receptors GENE, v. 317
Doi: http://doi.org/10.1016/S0378-1119(03)00666-8

2002

Brilli, M., Lio, P., Lazcano, A. and Fani, R., 2002. Evolution of TIM barrel: Multiple gene elongation events in HisA. Origins of Life and Evolution of the Biosphere, v. 22

Lio, P., 2002. Structure and evolution of the histidine biosynthetic pathway Origins of Life and Evolution of the Biosphere, v. 22

1999

Hagelberg, E., Kayser, M., Nagy, M., Roewer, L., Zimdahl, H., Krawczak, M., Lió, P. and Schiefenhövel, W., 1999. Molecular genetic evidence for the human settlement of the Pacific: analysis of mitochondrial DNA, Y chromosome and HLA markers. Philos Trans R Soc Lond B Biol Sci, v. 354
Doi: http://doi.org/10.1098/rstb.1999.0367

1997

Dewar, J., Wheatley, A., Wilkinson, J., Holgate, ST., Thomas, NS., Lio, P., Morton, NE. and Hall, IP., 1997. Association of the Gln 27 beta(2)-adrenoceptor polymorphism and IgE variability in asthmatic families CHEST, v. 111

Morton, NE. and Lio, P., 1997. Oligogenic linkage and map integration GENETIC MAPPING OF DISEASE GENES,

Thomas, NS., Wilkinson, J., Lio, P., Doull, I., Morton, NE. and Holgate, ST., 1997. Investigation of the genetic factors underlying asthma and atopy in outbred UK populations 5TH WEST-PACIFIC ALLERGY SYMPOSIUM / 7TH KOREA-JAPAN JOINT ALLERGY SYMPOSIUM,

1995

Bagnoli, F., Guasti, G. and Lio, P., 1995. Translation optimization in bacteria: Statistical models NONLINEAR EXCITATIONS IN BIOMOLECULES,

1994

Fani, R., Grifoni, A., Damiani, G., Lio, P. and Mori, E., 1994. Nucleotide Sequence of Azospirillum RAPD markers Azospirillum VI and Related Microorganisms:: Genetics - Physiology - Ecology (NATO ASI Series / Ecological Sciences),

Fani, R., Bandi, C., Bazzicalupo, M., Damiani, G., Di Cello, F., Fancelli, S., Gerace, L., Grifoni, A., Lio, P. and Mori, E., 1994. Phylogenetic Studies of the Genus Azospirillum Related Microorganisms:: Genetics - Physiology - Ecology (NATO ASI Series / Ecological Sciences),

Internet publications

2022

Igashov, I., Jamasb, A., Sadek, A., Sverrisson, F., Schneuing, A., Liò, P., Blundell, T., Bronstein, M. and Correia, B., 2022. Decoding Surface Fingerprints for Protein-Ligand Interactions
Doi: http://doi.org/10.1101/2022.04.26.489341

2020

Wang, D., Jamnik, M. and Lio, P., 2020. Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds

Zhao, Y., Wang, D., Gao, X., Mullins, R., Lio, P. and Jamnik, M., 2020. Probabilistic Dual Network Architecture Search on Graphs

2019

Luzhnica, E., Day, B. and Liò, P., 2019. On Graph Classification Networks, Datasets and Baselines

Viñas, R., Andrés-Terré, H., Liò, P. and Bryson, K., 2019. Adversarial generation of gene expression data
Doi: http://doi.org/10.1101/836254

Bica, I., Andrés-Terré, H., Cvejic, A. and Liò, P., 2019. Unsupervised generative and graph representation learning for modelling cell differentiation
Doi: http://doi.org/10.1101/801605

Books

2019

Lio, P. and Zuliani, P., 2019. Automated Reasoning for Systems Biology and Medicine Preface

Aiello, LM., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P. and Rocha, LM., 2019. Preface

2016

Bartocci, E., Lio, P. and Paoletti, N., 2016. Preface

2015

Di Serio, C., Liò, P., Nonis, A. and Tagliaferri, R., 2015. Preface

2011

Lio, P. and Verma, D., 2011. Biologically Inspired Networking and Sensing