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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

2024 (Published online)

  • 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: http://doi.org/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,
  • 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

  • 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
  • Dong, T., Jamnik, M. and Liò, P., 2024. Sphere Neural-Networks for Rational Reasoning. CoRR, v. abs/2403.15297
  • Buterez, D., Janet, JP., Oglic, D. and Lio, P., 2024. Masked Attention is All You Need for Graphs. CoRR, v. abs/2402.10793
  • Huang, K., Cao, W., Ta, H., Xiao, X. and Liò, P., 2024. Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach Proceedings of the ACM on Web Conference 2024,
    Doi: 10.1145/3589334.3645705
  • 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
  • Defilippo, A., Veltri, P., Lió, P. and Guzzi, PH., 2024. Leveraging graph neural networks for supporting Automatic Triage of Patients. CoRR, v. abs/2403.07038
  • 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
  • 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
  • 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, M-C., 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: http://doi.org/10.1016/j.nsa.2024.103937
  • 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
  • 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,
    Doi: http://doi.org/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
  • 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)

  • 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,
  • 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
  • 2023

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
    Doi: http://doi.org/10.1109/TPDS.2023.3317388
  • 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
    Doi: 10.1016/j.euroneuro.2023.01.001
  • 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
  • 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
  • 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
    Doi: http://doi.org/10.3934/ipi.2023049
  • Zou, X., Zhao, X., Liò, P. and Zhao, Y., 2023. Will More Expressive Graph Neural Networks do Better on Generative Tasks? CoRR, v. abs/2308.11978
  • 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
    Doi: http://doi.org/10.3389/fimmu.2023.1091941
  • 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
    Doi: http://doi.org/10.1038/s41586-023-06355-3
  • 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
    Doi: http://doi.org/10.1002/clt2.12306
  • Caso, F., Trappolini, G., Bacciu, A., Liò, P. and Silvestri, F., 2023. Renormalized Graph Neural Networks. CoRR, v. abs/2306.00707
  • 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
  • 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
  • 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
  • Bazaga, A., Liò, P. and Micklem, G., 2023. Unsupervised Fact Verification by Language Model Distillation. CoRR, v. abs/2309.16540
  • Ceccarelli, F., Giusti, L., Holden, SB. and Liò, P., 2023. Neural Embeddings for Protein Graphs. CoRR, v. abs/2306.04667
  • 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
  • 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. CoRR, v. abs/2306.06064
  • 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
  • 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
  • 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., Deng, Z., Wang, S., Chen, D., Hu, X., Liò, P., Schönlieb, C-B. and Avilés-Rivero, AE., 2023. TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios. CoRR, v. abs/2311.18839
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
    Doi: 10.1016/j.ifacol.2023.10.1457
  • 2022

  • 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
    Doi: http://doi.org/10.3390/app12188954
  • 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
    Doi: http://doi.org/10.1093/bioinformatics/btab804
  • 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
    Doi: http://doi.org/10.1681/ASN.2022010069
  • 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
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  • 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
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  • Viñas, R., Andrés-Terré, H., Liò, P. and Bryson, K., 2021 (Accepted for publication). Adversarial generation of gene expression data. Bioinformatics,
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  • 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,
  • 2021

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  • 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
  • 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),
  • Zhu, M., Lió, P. and Moss, J., 2021. Modular Neural Ordinary Differential Equations. CoRR, v. abs/2109.07359
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  • Magister, LC., Kazhdan, D., Singh, V. and Liò, P., 2021. GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
  • 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
  • Opolka, FL., Zhi, Y-C., Liò, P. and Dong, X., 2021. Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
  • 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
  • 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
    Doi: http://doi.org/10.1016/j.mad.2020.111426
  • Moss, J., Opolka, FL., Dumitrascu, B. and Lió, P., 2021. Approximate Latent Force Model Inference. CoRR, v. abs/2109.11851
  • Fanfani, V., Vinas Torne, R., Lio', P. and Stracquadanio, G., 2021. Discovering cancer driver genes and pathways using stochastic block model graph neural networks
    Doi: http://doi.org/10.1101/2021.06.29.450342
  • 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,
  • 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
    Doi: http://doi.org/10.1073/pnas.2021366118
  • 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
  • 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
  • Lipov, A. and Liò, P., 2021. A Multiscale Graph Convolutional Network Using Hierarchical Clustering Advances in Intelligent Systems and Computing, v. 1364 AISC
    Doi: http://doi.org/10.1007/978-3-030-73103-8_35
  • 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
    Doi: http://doi.org/10.1186/s12859-020-03937-0
  • 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
  • Zhou, B., Liu, X., Liu, Y., Huang, Y., Liò, P. and Wang, Y., 2021. Spectral Transform Forms Scalable Transformer
  • 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
    Doi: 10.13128/Substantia-914
  • 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
    Doi: http://doi.org/10.1007/978-1-0716-1641-3_16
  • 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
    Doi: http://doi.org/10.1155/2021/5308206
  • 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
  • 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
    Doi: http://doi.org/10.1093/bib/bbab100
  • Georgiev, D., Barbiero, P., Kazhdan, D., Veličković, P. and Liò, P., 2021. Algorithmic Concept-based Explainable Reasoning
  • 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
    Doi: http://doi.org/10.3389/fgene.2021.652907
  • 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
  • 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)

  • 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,
    Doi: http://doi.org/10.1038/s41598-020-79142-z
  • 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,
  • 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,
    Doi: http://doi.org/10.1007/978-3-030-59436-7_9
  • 2020

  • 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
    Doi: http://doi.org/10.1109/ACCESS.2020.2994516
  • Viñas, R., Azevedo, T., Gamazon, E. and Liò, P., 2020. Gene Expression Imputation with Generative Adversarial Imputation Nets
    Doi: http://doi.org/10.1101/2020.06.09.141689
  • 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
    Doi: http://doi.org/10.1101/2020.05.21.109082
  • Glass, S., Spasov, S. and Liò, P., 2020. RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow
  • 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
    Doi: http://doi.org/10.1101/2020.11.08.370288
  • 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,
    Doi: http://doi.org/10.1093/bib/bbaa173
  • Rocheteau, E., Liò, P. and Hyland, S., 2020. Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks
  • 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
    Doi: http://doi.org/10.1371/journal.pone.0228962
  • 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
    Doi: http://doi.org/10.1038/s41598-020-66878-x
  • 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
    Doi: http://doi.org/10.1038/s41398-020-01075-y
  • 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
    Doi: http://doi.org/10.7544/issn1000-1239.2020.20190754
  • 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
    Doi: http://doi.org/10.1016/j.eswa.2020.113661
  • 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
  • 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
    Doi: http://doi.org/10.1016/j.pmcj.2020.101230
  • 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
    Doi: http://doi.org/10.1016/j.future.2020.06.020
  • 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
    Doi: http://doi.org/10.1101/2020.07.15.204701
  • 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
    Doi: http://doi.org/10.1109/TNNLS.2019.2945992
  • 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
    Doi: http://doi.org/10.1007/978-3-030-49161-1_15
  • Spasov, SE. and Liò, P., 2020. Dynamic neural network channel execution for efficient training 30th British Machine Vision Conference 2019, BMVC 2019,
  • 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
    Doi: http://doi.org/10.1101/2020.06.04.20122424
  • Flood, PDL., Viñas, R. and Liò, P., 2020. Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction
  • Spasov, S., Stefano, AD., Lio, P. and Tang, J., 2020. GRADE: Graph Dynamic Embedding
  • Stankevičiūtė, K., Azevedo, T., Campbell, A., Bethlehem, R. and Liò, P., 2020. Population Graph GNNs for Brain Age Prediction
    Doi: http://doi.org/10.1101/2020.06.26.172171
  • Wichitwechkarn, V., Day, B., Bodnar, C., Wales, M. and Liò, P., 2020. The Role of Isomorphism Classes in Multi-Relational Datasets
  • Del Prete, E., Facchiano, A. and Liò, P., 2020. Bioinformatics methodologies for coeliac disease and its comorbidities. Brief Bioinform, v. 21
    Doi: 10.1093/bib/bby109
  • 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
  • Müller, TT. and Lio, P., 2020. PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases. Front Artif Intell, v. 3
    Doi: http://doi.org/10.3389/frai.2020.00023
  • 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
  • Lu, X., Liao, Y., Lio, P. and Hui, P., 2020. Privacy-preserving asynchronous federated learning mechanism for edge network computing IEEE Access, v. 8
    Doi: http://doi.org/10.1109/ACCESS.2020.2978082
  • 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
  • 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
    Doi: http://doi.org/10.1038/s41598-020-57916-9
  • 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,
    Doi: http://doi.org/10.1109/IJCNN48605.2020.9207564
  • Bodnar, C., Day, B. and Lió, P., 2020. Proximal distilled evolutionary reinforcement learning AAAI 2020 - 34th AAAI Conference on Artificial Intelligence,
  • Wang, D., Jamnik, M. and Lio, P., 2020. ABSTRACT DIAGRAMMATIC REASONING WITH MULTIPLEX GRAPH NETWORKS 8th International Conference on Learning Representations, ICLR 2020,
  • 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
  • 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
    Doi: http://doi.org/10.1002/cpe.5683
  • 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
  • Moss, J. and Lió, P., 2020. Gene Regulatory Network Inference with Latent Force Models
  • Karavias, V., Day, B. and Liò, P., 2020. Uncertainty in Neural Relational Inference Trajectory Reconstruction
  • 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
    Doi: http://doi.org/10.1016/j.cose.2020.101861
  • Campbell, A. and Liò, P., 2020. tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder
  • 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
    Doi: http://doi.org/10.1016/j.pmcj.2020.101230
  • Buterez, D., Bica, I., Tariq, I., Andrés-Terré, H. and Liò, P., 2020. CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks
    Doi: http://doi.org/10.1101/2020.12.20.423645
  • Georgiev, D. and Liò, P., 2020. Neural Bipartite Matching
  • Barbiero, P. and Lió, P., 2020. The computational Patient has diabetes and a COVID
    Doi: http://doi.org/10.1101/2020.06.10.20127183
  • 2019 (Published online)

  • 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
    Doi: http://doi.org/10.1186/s12859-019-2691-y
  • 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
  • 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
    Doi: 10.1609/aaai.v33i01.33016900
  • Müller, T. and Lió, P., 2019 (Published online). Personalisable Clinical Decision Support System. ERCIM News, v. 116
  • 2019 (Accepted for publication)

  • 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,
  • 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,
  • 2019

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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: http://doi.org/10.1101/859496
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

  • 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
  • Felicetti, L., Femminella, M., Reali, G. and Liò, P., 2018. Applications of molecular communications to medicine: a survey. CoRR, v. abs/1808.04242
  • 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,
  • 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
  • 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
  • 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
  • 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
  • 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
  • Cangea, C., Grauslys, A., Liò, P. and Falciani, F., 2018. Structure-Based Networks for Drug Validation
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 2017 (Accepted for publication)

  • 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,
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

  • 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
  • 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
  • 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
  • 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),
  • 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
  • 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
  • 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
  • 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
  • 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
  • 2015

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
    Doi: http://doi.org/10.1093/bioinformatics/btu731
  • 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
  • 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
  • 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
  • 2014

  • 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
  • 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,
    Doi: http://doi.org/10.1007/s00779-014-0793-0
  • 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
  • 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
  • Nardi, F., Liò, P., Carapelli, A. and Frati, F., 2014. MtPAN<sup>3</sup>: 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
  • Pratanwanich, N. and Lio, P., 2014. Exploring the complexity of pathway-drug relationships using latent Dirichlet allocation. Comput Biol Chem, v. 53 Pt A
    Doi: http://doi.org/10.1016/j.compbiolchem.2014.08.019
  • 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
  • Azad, S. and Lio, P., 2014. Emerging trends of malaria-dengue geographical coupling in the Southeast Asia region. J Vector Borne Dis, v. 51
  • 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,
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Angione, C., Carapezza, G., Costanza, J., Lió, P. and Nicosia, G., 2013. Design and strain selection criteria for bacterial communication networks Nano Communication Networks,
  • 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
  • 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<sub>+</sub> T cells RETROVIROLOGY, v. 10
    Doi: 10.1186/1742-4690-10-S1-P114
  • 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. 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
  • 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
  • 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., 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
  • 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
  • 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
  • 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
  • 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. Pareto Optimality in Organelle Energy Metabolism Analysis. IEEE/ACM Trans Comput Biol Bioinform,
  • 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,
  • 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., 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
  • 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

  • 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
  • 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
  • Liò, P., Merelli, E. and Paoletti, N., 2012. Disease processes as hybrid dynamical systems EPTCS 92, 2012, pp. 152-166,
  • 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,
  • 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
  • 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
  • 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
  • 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
  • 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: http://doi.org/10.1016/j.cnsns.2012.03.023
  • 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
  • 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, X., Pan, H. and Lio, P., 2012. High Delivery Performance Opportunistic Routing Scheme for Delay Tolerant Networks CHINA COMMUNICATIONS, v. 9
  • 2011

  • Balocco, C. and Lio, P., 2011. Assessing ventilation system performance in isolation rooms ENERG BUILDINGS, v. 43
    Doi: http://doi.org/10.1016/j.enbuild.2010.09.020
  • 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
  • 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
  • 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
  • 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
  • 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. 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
  • 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
  • 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., 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
    Doi: http://doi.org/10.1093/nar/gkq1226
  • Leung, IXY., Chan, S-Y., Hui, P. and Lio', P., 2011. Intra-City Urban Network and Traffic Flow Analysis from GPS Mobility Trace
  • 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
  • 2010

  • Liò, P. and Verma, DC., 2010. Biologically inspired networking [Guest Editorial]. IEEE Netw., v. 24
    Doi: http://doi.org/10.1109/MNET.2010.5464220
  • Lio, P. and Verma, D., 2010. Guest Editorial: Biologically inspired networking IEEE Network, v. 24
    Doi: http://doi.org/10.1109/MNET.2010.5464220
  • Aldinucci, M., Bracciali, A. and Lio, P., 2010. Formal Synthetic Immunology Ercim News, v. 82
  • 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
  • Guazzini, A., Lió, P., Bagnoli, F., Passarella, A. and Conti, M., 2010. Cognitive network dynamics in chatlines Procedia Computer Science, v. 1
    Doi: http://doi.org/10.1016/j.procs.2010.04.265
  • 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
  • Kitchovitch, S. and Liò, P., 2010. Risk perception and disease spread on social networks Procedia Computer Science, v. 1
    Doi: http://doi.org/10.1016/j.procs.2010.04.264
  • 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
  • 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,
    Doi: http://doi.org/10.1109/MLSP.2010.5588995
  • Lio, P. and Verma, D., 2010. Biologically Inspired Networking IEEE NETWORK, v. 24
  • Sorathiya, A., Bracciali, A. and Liò, P., 2010. An integrated modelling approach for R5-X4 mutation and HAART therapy assessment Swarm Intelligence,
  • Balocco, C. and Lio, P., 2010. Modelling infection spreading control in a Hospital isolation room Journal of Biomedical Science and Engineering, v. 3
    Doi: http://doi.org/10.4236/jbise.2010.37089
  • 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,
  • 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
  • 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
  • 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
  • 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
    Doi: http://doi.org/10.1074/mcp.M900372-MCP200
  • 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
  • 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
    Doi: http://doi.org/10.1038/msb.2010.79
  • 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
    Doi: http://doi.org/10.1186/1471-2105-11-S1-S67
  • 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

  • Bianchi, L. and Lio, P., 2009. La legge e il DNA Le Scienze, Italian Edition Scientific American,
  • 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
  • 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,
    Doi: http://doi.org/10.1155/2009/256714
  • 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
    Doi: http://doi.org/10.1007/s00239-009-9286-6
  • 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
    Doi: http://doi.org/10.1186/1471-2105-10-S12-S6
  • 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
    Doi: http://doi.org/10.1016/j.cell.2009.06.020
  • 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
    Doi: http://doi.org/10.1371/journal.pone.0006972
  • 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
  • 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
    Doi: http://doi.org/10.1016/j.adhoc.2008.03.009
  • Leung, IXY., Hui, P., Lio, P. and Crowcroft, J., 2009. Towards real-time community detection in large networks PHYS REV E, v. 79
    Doi: http://doi.org/10.1103/PhysRevE.79.066107
  • 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
  • Milanesi, L., Romano, P., Castellani, G., Remondini, D. and Liò, P., 2009. Trends in modeling Biomedical Complex Systems. BMC Bioinformatics, v. 10 Suppl 12
    Doi: http://doi.org/10.1186/1471-2105-10-S12-I1
  • Milanesi, L., Romano, P., Castellani, G., Remondini, D. and Lio, P., 2009. Trends in modeling Biomedical Complex Systems BMC BIOINFORMATICS, v. 10
    Doi: http://doi.org/10.1186/1471-2105-10-S12-11
  • 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
  • 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
    Doi: http://doi.org/10.1016/j.cell.2008.10.048
  • Bagnoli, F., Guazzini, A. and Liò, P., 2008. Human Heuristics for Autonomous Agents CoRR, v. abs/0801.3048
  • Lio, P. and Bishop, M., 2008. Modeling sequence evolution Methods Mol Biol., v. 452
  • 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
    Doi: http://doi.org/10.1186/1471-2105-9-551
  • Liò, P. and Bishop, M., 2008. Modeling sequence evolution. Methods Mol Biol, v. 452
    Doi: http://doi.org/10.1007/978-1-60327-159-2_13
  • 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
  • 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
    Doi: http://doi.org/10.1117/12.783462
  • 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
    Doi: http://doi.org/10.1371/journal.pcbi.1000135
  • 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
  • Lu, YE., Lió, P. and Hand, S., 2008. On low dimensional random projections and similarity search International Conference on Information and Knowledge Management, Proceedings,
    Doi: http://doi.org/10.1145/1458082.1458182
  • Xie, S., Lawniczak, AT. and Lió, P., 2008. Parametric &amp; 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
    Doi: http://doi.org/10.1093/bib/bbm051
  • 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,
    Doi: http://doi.org/10.1145/1456403.1456407
  • 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
  • 2007

  • Bianchi, L. and Lio, P., 2007. Forensic DNA and bioinformatics BRIEF BIOINFORM, v. 8
    Doi: http://doi.org/10.1093/bib/bbm006
  • 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
    Doi: http://doi.org/10.1016/j.cub.2007.01.068
  • Papetti, C., Lio, P., Ruber, L., Patarnello, T. and Zardoya, R., 2007. Antarctic fish mitochondrial genomes lack ND6 gene J MOL EVOL, v. 65
    Doi: http://doi.org/10.1007/s00239-007-9030-z
  • Liò, P., 2007. Topological and dynamical properties of genetic and social networks PAMM, v. 7
    Doi: 10.1002/pamm.200700842
  • 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
  • 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
  • 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
    Doi: http://doi.org/10.1093/bioinformatics/btl607
  • 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
    Doi: http://doi.org/10.1371/journal.pcbi.0030103
  • 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
    Doi: http://doi.org/10.1186/1471-2202-7-S1-S8
  • Bagnoli, F., Lio, P. and Sguanci, L., 2006. Modeling viral coevolution: HIV multi-clonal persistence and competition dynamics PHYSICA A, v. 366
    Doi: http://doi.org/10.1016/j.physa.2005.10.055
  • 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
    Doi: http://doi.org/10.1007/s00239-006-0074-2
  • 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. 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
  • Sguanci, L., Bagnoli, F. and Lio, P., 2006. Mathematical Model of HIV superinfection dynamics and R5 to X4 switch
  • 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
    Doi: http://doi.org/10.1007/s00239-004-0198-1
  • Piazza, F. and Lio, P., 2005. Statistical analysis of simple repeats in the human genome PHYSICA A, v. 347
    Doi: http://doi.org/10.1016/j.physa.2004.08.038
  • 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
  • Tadesse, MG., Vannucci, M. and Lio, P., 2004. Identification of DNA regulatory motifs using Bayesian variable selection BIOINFORMATICS, v. 20
    Doi: http://doi.org/10.1093/bioinformatics/bth282
  • 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
    Doi: http://doi.org/10.1038/ng1377
  • 2003

  • Lio, P., 2003. Wavelets in bioinformatics and computational biology: state of art and perspectives BIOINFORMATICS, v. 19
  • Lio, P., 2003. Il genoma della Sars Le Scienze Italian Edition of Scientific American, v. June 2003
  • Lio, P., 2003. Statistical bioinformatic methods in microbial genome analysis BIOESSAYS, v. 25
    Doi: http://doi.org/10.1002/bies.10231
  • Liò, P., 2003. Dimensionality and dependence problems in statistical genomics. Brief Bioinform, v. 4
    Doi: http://doi.org/10.1093/bib/4.2.168
  • 2002

  • Liò, P. and Goldman, N., 2002. Modeling mitochondrial protein evolution using structural information. J Mol Evol, v. 54
    Doi: http://doi.org/10.1007/s00239001-0052-7
  • Lio, P., 2002. Investigating the relationship between genome structure, composition, and ecology in prokaryotes MOL BIOL EVOL, v. 19
  • Lio, P., 2002. Una vita per le proteine Le Scienze Italian Edition of Scientific American, v. February 2002
  • Lio, P. and Goldman, N., 2002. Modeling mitochondrial protein evolution using structural information J MOL EVOL, v. 54
    Doi: http://doi.org/10.1007/s00239001-0052-7
  • 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
  • 2001

  • Lio, P., 2001. Dal Genoma al Fisioma
  • Lio, P., 2001. Le nuove sfide della filogenesi molecolare Le Scienze Italian Edition of Scientific American, v. February 2001
  • Massingham, T., Davies, LJ. and Lio, P., 2001. Analysing gene function after duplication BIOESSAYS, v. 23
  • 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
  • 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
  • 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
    Doi: http://doi.org/10.1093/bioinformatics/16.4.376
  • Lio, P. and Vannucci, M., 2000. Finding pathogenicity islands and gene transfer events in genome data BIOINFORMATICS, v. 16
  • Lio, P., 2000. Siamo uomini non DNA robot,
  • 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
    Doi: http://doi.org/10.1007/s002390010061
  • 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
  • 1999

  • Liò, P. and Goldman, N., 1999. Using protein structural information in evolutionary inference: transmembrane proteins. Mol Biol Evol, v. 16
    Doi: http://doi.org/10.1093/oxfordjournals.molbev.a026083
  • 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
    Doi: http://doi.org/10.1016/s0923-2508(99)80026-6
  • 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
    Doi: http://doi.org/10.1098/rspb.1999.0663
  • 1998

  • Lio, P. and Ruffo, S., 1998. Searching for genomic constraints NUOVO CIMENTO D, v. 20
  • Liò, P., Goldman, N., Thorne, JL. and Jones3, DT., 1998. PASSML: combining evolutionary inference and protein secondary structure prediction. Bioinformatics, v. 14
    Doi: http://doi.org/10.1093/bioinformatics/14.8.726
  • Liò, P., Goldman, N., Thorne, JL. and Jones, DT., 1998. PASSML: Combining evolutionary inference and protein secondary structure prediction Bioinformatics, v. 14
    Doi: http://doi.org/10.1093/bioinformatics/14.8.726
  • Liò, P. and Goldman, N., 1998. Review: Models of molecular evolution and phylogeny Genome Research, v. 8
    Doi: http://doi.org/10.1101/gr.8.12.1233
  • Liò, P. and Goldman, N., 1998. Models of molecular evolution and phylogeny. Genome Res, v. 8
    Doi: http://doi.org/10.1101/gr.8.12.1233
  • 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
    Doi: http://doi.org/10.1378/chest.111.6_supplement.78s
  • 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
    Doi: http://doi.org/10.1016/s0378-1119(97)00146-7
  • 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
    Doi: http://doi.org/10.1073/pnas.94.10.5344
  • Bogani, P., Liò, P., Intrieri, MC. and Buiatti, M., 1997. A physiological and molecular analysis of the genus Nicotiana. Mol Phylogenet Evol, v. 7
    Doi: http://doi.org/10.1006/mpev.1996.0356
  • 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
    Doi: http://doi.org/10.1016/s0091-6749(97)70234-3
  • 1996

  • 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., 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
    Doi: http://doi.org/10.1128/mr.60.1.44-69.1996
  • 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

  • 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
  • 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
  • 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
  • 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)

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

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

  • 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
  • 2023

  • 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
  • Opolka, FL., Zhi, YC., Liò, P. and Dong, X., 2023. Graph Classification Gaussian Processes via Spectral Features Proceedings of Machine Learning Research, v. 216
  • Duta, I., Cassarà, G., Silvestri, F. and Lió, P., 2023. Sheaf Hypergraph Networks. NeurIPS,
  • 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,
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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,
  • 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)

  • 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
  • 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
  • 2022

  • 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
  • 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,
  • 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
  • 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
  • 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
  • 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
  • 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
  • Opolka, FL. and Liò, P., 2022. Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes Proceedings of Machine Learning Research, v. 151
  • 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., 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,
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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,
  • 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
  • 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
  • 2021 (Accepted for publication)

  • Norcliffe, A., Bodnar, C., Day, B., Moss, J. and Liò, P., 2021 (Accepted for publication). Neural ODE Processes
  • Drotár, P., Jamasb, AR., Day, B., Cangea, C. and Liò, P., 2021 (Accepted for publication). Structure-aware generation of drug-like molecules
  • 2021

  • 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
  • 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
  • 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
  • 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
  • Beaini, D., Passaro, S., Létourneau, V., Hamilton, WL., Corso, G. and Liò, P., 2021. Directional Graph 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),
  • 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
  • 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
  • Qendro, L., Campbell, A., Liò, P. and Mascolo, C., 2021. Early Exit Ensembles for Uncertainty Quantification Proceedings of Machine Learning Research, v. 158
  • Kazhdan, D., Dimanov, B., Terre, HA., Jamnik, M., Liò, P. and Weller, A., 2021. Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
  • 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
  • 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

  • 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
  • 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,
  • 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
  • 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., 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
  • 2019 (No publication date)

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

  • 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
  • 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
  • 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,
  • 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
  • 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
  • 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,
  • 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,
  • 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,
    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
  • 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,
  • 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
  • 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
  • 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
  • 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

  • Velickovic, P., Wang, D., Lane, ND. and Liò, P., 2016. X-CNN: Cross-modal convolutional neural networks for sparse datasets. SSCI,
  • 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
  • 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
  • 2015

  • 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 <i>NuChart</i>-<i>II</i> 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
  • 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
  • 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)

  • 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
  • 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),
  • 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
  • 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
  • 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

  • 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
  • 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,
  • 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,
  • 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,
  • 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
  • Merelli, E., Paoletti, N. and Lio, P., 2011. Methodological Bridges for Multi-Level Systems Procedia Computer Science, v. 7
  • Lio, P., Emanuela Merelli, and Nicola Paoletti, NP., 2011. Multiple verification in computational modeling of bone pathologies EPTCS, v. 67
  • 2010

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Bella, G. and Lio, P., 2009. Formal Analysis of the Genetic Toggle COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 5688
  • 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
  • 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)
  • 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., 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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,
  • 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, JF., Molva, R., Passarella, A., Stavrakakis, I. and Whitaker, RM., 2008. Social Networking for Pervasive Adaptation. SASO Workshops,
  • 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,
  • 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
  • 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
  • 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,
  • 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
  • Fani, R., Caramelli, D. and Lio, P., 2006. It happened... From prebiotic chemistry to human evolution Rivista di biologia,
  • Sguanci, L., Lio, P. and Bagnoli, F., 2006. Modeling evolutionary dynamics of HIV infection COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 4210
  • 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),
  • Book chapters

    2023

  • 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
  • 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
  • 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
  • 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

  • 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
  • Lio, P., Bianchi, L., Nguyen, V. and Kitchovich, S., 2013. Risk Perception, Heuristics and Epidemic Spread
  • 2012

  • Lio, P. and Verma, D., 2012. Biologically Inspired Networking and Sensing: Algorithms and Architectures Preface
  • 2010

  • 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
  • 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
  • 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, J., LiÒ, P. and Frati, F., 2005. Relationships between hexapods and crustaceans based on four mitochondrial genes
    Doi: 10.1201/9781420037548.ch12
  • Carapelli, A., Nardi, F., Dallai, R., Boore, JL., Lio, P. and Frati, F., 2005. Relationships between hexapods and crustaceans based on 4 mitochondrial genes
  • 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
  • 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

  • Zhao, Y., Wang, D., Gao, X., Mullins, R., Lio, P. and Jamnik, M., 2020. Probabilistic Dual Network Architecture Search on Graphs
  • Wang, D., Jamnik, M. and Lio, P., 2020. Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds
  • 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

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