Journal articles
Bali, A., Wolter, S., Pelzel, D., Weyer, U., Azevedo, T., Lio, P., Kouka, M., Geißler, K., Bitter, T., Ernst, G., Xylander, A., Ziller, N., Mühlig, A., von Eggeling, F., Guntinas-Lichius, O. and Pertzborn, D., 2025 (Published online). Real-Time Intraoperative Decision-Making in Head and Neck Tumor Surgery: A Histopathologically Grounded Hyperspectral Imaging and Deep Learning Approach Cancers, v. 17
Doi: http://doi.org/10.3390/cancers17101617
Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lio, P., Lepri, B. and Passerini, A., 2025. Explaining the Explainers in Graph Neural Networks: a Comparative Study ACM Computing Surveys, v. 57
Doi: 10.1145/3696444
Raisa, RA., Rodela, AS., Yousuf, MA., Azad, A., Alyami, SA., Liò, P., Islam, MZ., Pogrebna, G. and Moni, MA., 2025. Corrections to “Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study” IEEE Access, v. 13
Doi: http://doi.org/10.1109/access.2025.3551391
Hasan, MM., Abdar, M., Khosravi, A., Aickelin, U., Lio, P., Hossain, I., Rahman, A. and Nahavandi, S., 2025. Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing ACM Computing Surveys, v. 57
Doi: 10.1145/3727633
Crowley, R., Parkin, K., Rocheteau, E., Massou, E., Friedmann, Y., John, A., Sippy, R., Liò, P. and Moore, A., 2025. Machine learning for prediction of childhood mental health problems in social care. BJPsych Open, v. 11
Doi: http://doi.org/10.1192/bjo.2025.32
Aucello, R., Pernice, S., Tortarolo, D., Calogero, RA., Herrera-Rincon, C., Ronchi, G., Geuna, S., Cordero, F., Lió, P. and Beccuti, M., 2025. UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale. Bioinformatics, v. 41
Doi: http://doi.org/10.1093/bioinformatics/btaf103
El, B., Choudhury, D., Liò, P. and Joshi, CK., 2025. Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs. CoRR, v. abs/2502.12352
Prinzi, F., Barbiero, P., Greco, C., Amorese, T., Cordasco, G., Liò, P., Vitabile, S. and Esposito, A., 2025. Using AI explainable models and handwriting/drawing tasks for psychological well-being Information Systems, v. 127
Doi: 10.1016/j.is.2024.102465
Lomoio, U., Veltri, P., Guzzi, PH. and Liò, P., 2025. Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution. Artif Intell Med, v. 160
Doi: http://doi.org/10.1016/j.artmed.2024.103058
Sun, B. and Liò, P., 2025. EU-Nets: Enhanced, Explainable and Parsimonious U-Nets. CoRR, v. abs/2502.18122
Yan, C., Lu, X., Lio, P., Hui, P. and He, D., 2025. Self-Simulation and Meta-Model Aggregation-Based Heterogeneous-Graph-Coupled Federated Learning IEEE Internet of Things Journal, v. 12
Doi: 10.1109/JIOT.2024.3462724
Ali, R., Caso, F., Irwin, C. and Liò, P., 2025. Entropy-Lens: The Information Signature of Transformer Computations. CoRR, v. abs/2502.16570
Sun, B. and Liò, P., 2025. Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers. CoRR, v. abs/2501.01311
Li, K., Zheng, J., Ni, W., Huang, H., Lio, P., Dressler, F. and Akan, OB., 2025. Biasing Federated Learning with a New Adversarial Graph Attention Network IEEE Transactions on Mobile Computing, v. 24
Doi: http://doi.org/10.1109/TMC.2024.3499371
Carli, F., Di Chiaro, P., Morelli, M., Arora, C., Bisceglia, L., De Oliveira Rosa, N., Cortesi, A., Franceschi, S., Lessi, F., Di Stefano, AL., Santonocito, OS., Pasqualetti, F., Aretini, P., Miglionico, P., Diaferia, GR., Giannotti, F., Liò, P., Duran-Frigola, M., Mazzanti, CM., Natoli, G. and Raimondi, F., 2025. Learning and actioning general principles of cancer cell drug sensitivity. Nat Commun, v. 16
Doi: http://doi.org/10.1038/s41467-025-56827-5
Lee, CK., Jeha, P., Frellsen, J., Lio, P., Albergo, MS. and Vargas, F., 2025. Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo. CoRR, v. abs/2502.06079
Zahoor, S., Liò, P., Dias, G. and Hasanuzzaman, M., 2025. Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data. CoRR, v. abs/2501.15973
Telyatnikov, L., Bucarelli, MS., Bernardez, G., Zaghen, O., Scardapane, S. and Liò, P., 2025. Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design Transactions on Machine Learning Research, v. 2025-February
Mamalakis, M., Mamalakis, A., Agartz, I., Mørch-Johnsen, LE., Murray, GK., Suckling, J. and Lio, P., 2025. Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks AI Open, v. 6
Doi: http://doi.org/10.1016/j.aiopen.2025.02.001
Orka, NA., Awal, MA., Liò, P., Pogrebna, G., Ross, AG. and Moni, MA., 2025. Quantum deep learning in neuroinformatics: a systematic review Artificial Intelligence Review, v. 58
Doi: http://doi.org/10.1007/s10462-025-11136-7
Wang, R., Tian, Y., Liò, P. and Bianconi, G., 2025. Dirac-equation signal processing: Physics boosts topological machine learning PNAS Nexus, v. 4
Doi: 10.1093/pnasnexus/pgaf139
Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lio, P., Lepri, B. and Passerini, A., 2025. Explaining the Explainers in Graph Neural Networks: a Comparative Study. ACM Comput. Surv., v. 57
Minelli, A., Meloni, A., Bortolomasi, M., Pisanu, C., Zampieri, E., Congiu, D., Lana, B., Manchia, M., Meattini, M., Paribello, P., Baune, BT., Serretti, A., Dierssen, M., Maron, E., Potier, MC., Gennarelli, M., van Westrhenen, R., Squassina, A., Stacey, D., Mehta, D., Janzing, JGE., Ebert, B., Fabbri, C., Lio’, P. and Rybakowski, F., 2025. Telomere length and mitochondrial DNA copy number in association with trauma-focused psychotherapy efficacy Neuroscience Applied, v. 4
Doi: http://doi.org/10.1016/j.nsa.2024.104095
Bi, X., Tang, S., Xiao, B., Li, W., Gao, X. and Liò, P., 2025. A Systematic Review of Heart Sound Detection Algorithms: Experimental Results and Insights IEEE Transactions on Instrumentation and Measurement, v. 74
Doi: http://doi.org/10.1109/TIM.2025.3547082
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Lió, P., 2024 (Published online). Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting Nature Communications, v. 15
Doi: 10.1038/s41467-024-45566-8
Thaventhiran, J., 2024 (Accepted for publication). Intratumoral antigen signaling traps CD8+ T cells to confine exhaustion to the tumor site Science Immunology,
Doi: 10.1126/sciimmunol.ade2094
Siebenmorgen, T., Menezes, F., Benassou, S., Merdivan, E., Didi, K., Mourão, ASD., Kitel, R., Liò, P., Kesselheim, S., Piraud, M., Theis, FJ., Sattler, M. and Popowicz, GM., 2024. MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery. Nat Comput Sci, v. 4
Doi: 10.1038/s43588-024-00627-2
Bergna, R., Calvo-Ordoñez, S., Opolka, FL., Liò, P. and Hernández-Lobato, JM., 2024. Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations. CoRR, v. abs/2408.16115
Shen, X., Lio, P., Yang, L., Yuan, R., Zhang, Y. and Peng, C., 2024. Graph Rewiring and Preprocessing for Graph Neural Networks Based on Effective Resistance IEEE Transactions on Knowledge and Data Engineering, v. 36
Doi: 10.1109/TKDE.2024.3397692
Lawrence, E., El-Shazly, A., Seal, S., Joshi, CK., Liò, P., Singh, S., Bender, A., Sormanni, P. and Greenig, M., 2024. Understanding Biology in the Age of Artificial Intelligence. CoRR, v. abs/2403.04106
Yang, Z., Zhang, G., Wu, J., Yang, J., Sheng, QZ., Xue, S., Zhou, C., Aggarwal, C., Peng, H., Hu, W., Hancock, E. and Liò, P., 2024. State of the Art and Potentialities of Graph-level Learning ACM Computing Surveys, v. 57
Doi: 10.1145/3695863
Duta, I. and Liò, P., 2024. SPHINX: Structural Prediction using Hypergraph Inference Network. CoRR, v. abs/2410.03208
Liu, L., Cheng, Y., Deng, Z., Wang, S., Chen, D., Hu, X., Liò, P., Schönlieb, CB. and Aviles-Rivero, A., 2024. TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios MM 2024 - Proceedings of the 32nd ACM International Conference on Multimedia,
Doi: 10.1145/3664647.3681153
Gantz, M., Mathis, SV., Nintzel, FEH., Lio, P. and Hollfelder, F., 2024. On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering. Faraday Discuss, v. 252
Doi: 10.1039/d4fd00065j
Mamalakis, M., Mamalakis, A., Agartz, I., Mørch-Johnsen, LE., Murray, GK., Suckling, J. and Lio, P., 2024. Solving the enigma: Deriving optimal explanations of deep networks. CoRR, v. abs/2405.10008
Rathod, S., Lio, P. and Zhang, X., 2024. Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes. CoRR, v. abs/2410.14426
Ali, R., Kulyte, P., Sáez de Ocáriz Borde, H. and Liò, P., 2024. Metric Learning for Clifford Group Equivariant Neural Networks Proceedings of Machine Learning Research, v. 251
Georgiev, D., Wilson, JJ., Buffelli, D. and Liò, P., 2024. Deep Equilibrium Algorithmic Reasoning. CoRR, v. abs/2410.15059
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: 10.1016/j.neucom.2024.127598
Du, Y., Jamasb, AR., Guo, J., Fu, T., Harris, C., Wang, Y., Duan, C., Liò, P., Schwaller, P. and Blundell, TL., 2024. Machine learning-aided generative molecular design Nature Machine Intelligence, v. 6
Doi: 10.1038/s42256-024-00843-5
Kujawa, Z., Poole, J., Georgiev, D., Numeroso, D. and Liò, P., 2024. Neural Algorithmic Reasoning with Multiple Correct Solutions. CoRR, v. abs/2409.06953
Yang, L., Liò, P., Shen, X., Zhang, Y. and Peng, C., 2024. Adaptive multi-scale Graph Neural Architecture Search framework Neurocomputing, v. 599
Doi: 10.1016/j.neucom.2024.128094
Caralt, FH., Gil, GB., Duta, I., Liò, P. and Cot, EA., 2024. Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks Proceedings of Machine Learning Research, v. 251
Barucci, A., Ciacci, G., Liò, P., Azevedo, T., Di Cencio, A., Merella, M., Bianucci, G., Bosio, G., Casati, S. and Collareta, A., 2024. An explainable Convolutional Neural Network approach to fossil shark tooth identification Bollettino della Societa Paleontologica Italiana, v. 63
Doi: http://doi.org/10.4435/BSPI.2024.15
Li, M., Micheli, A., Wang, YG., Pan, S., Lio, P., Gnecco, GS. and Sanguineti, M., 2024. Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications IEEE Transactions on Neural Networks and Learning Systems, v. 35
Doi: 10.1109/TNNLS.2024.3371592
Wang, Z., Ma, J., Gao, Q., Bain, C., Imoto, S., Liò, P., Cai, H., Chen, H. and Song, J., 2024. Dual-stream multi-dependency graph neural network enables precise cancer survival analysis. Med Image Anal, v. 97
Doi: 10.1016/j.media.2024.103252
Kulyte, P., Vargas, F., Mathis, SV., Wang, YG., Hernández-Lobato, JM. and Liò, P., 2024. Improving Antibody Design with Force-Guided Sampling in Diffusion Models. CoRR, v. abs/2406.05832
Braithwaite, L., Duta, I. and Liò, P., 2024. Heterogeneous Sheaf Neural Networks. CoRR, v. abs/2409.08036
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
Huang, K., Wang, YG., Li, M. and Liò, P., 2024. How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing Proceedings of Machine Learning Research, v. 235
Su, S., Duta, I., Magister, LC. and Liò, P., 2024. Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts. CoRR, v. abs/2410.07764
Georgiev, D., Wilson, JJ., Buffelli, D. and Liò, P., 2024. Deep Equilibrium Algorithmic Reasoning Advances in Neural Information Processing Systems, v. 37
Dong, T., Jamnik, M. and Liò, P., 2024. Sphere Neural-Networks for Rational Reasoning. CoRR, v. abs/2403.15297
Raisa, RA., Rodela, AS., Yousuf, MA., Azad, A., Alyami, SA., Lio, P., Islam, MZ., Pogrebna, G. and Moni, MA., 2024. Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study IEEE Access, v. 12
Doi: 10.1109/ACCESS.2024.3426928
Lope, EGD., Deshpande, S., Torné, RV., Liò, P., Glaab, E. and Bordas, SPA., 2024. Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease. CoRR, v. abs/2406.14442
Sabari, A., Hasan, I., Alyami, SA., Liò, P., Ali, MS., Moni, MA. and Azad, AKM., 2024. LandSin: A differential ML and google API-enabled web server for real-time land insights and beyond[Formula presented] Software Impacts, v. 22
Doi: http://doi.org/10.1016/j.simpa.2024.100718
Zaki, JK., Tomasik, J., McCune, JA., Bahn, S., Liò, P. and Scherman, OA., 2024. Explainable Deep Learning Framework for SERS Bio-quantification. CoRR, v. abs/2411.08082
Defilippo, A., Veltri, P., Lió, P. and Guzzi, PH., 2024. Leveraging graph neural networks for supporting automatic triage of patients. Sci Rep, v. 14
Doi: 10.1038/s41598-024-63376-2
Rowbottom, J., Maierhofer, G., Deveney, T., Schratz, K., Liò, P., Schönlieb, C-B. and Budd, CJ., 2024. G-Adaptive mesh refinement - leveraging graph neural networks and differentiable finite element solvers. CoRR, v. abs/2407.04516
Schneuing, A., Harris, C., Du, Y., Didi, K., Jamasb, A., Igashov, I., Du, W., Gomes, C., Blundell, TL., Lio, P., Welling, M., Bronstein, M. and Correia, B., 2024. Structure-based drug design with equivariant diffusion models. Nat Comput Sci, v. 4
Doi: http://doi.org/10.1038/s43588-024-00737-x
Singh, V., Khanzadeh, M., Davis, V., Rush, H., Rossi, E., Shrader, J. and Lio, P., 2024. Bayesian Binary Search. CoRR, v. abs/2410.01771
Buterez, D., Janet, JP., Oglic, D. and Lio, P., 2024. Masked Attention is All You Need for Graphs. CoRR, v. abs/2402.10793
Zhao, X., Li, Z., Shen, M., Stan, G-B., Liò, P. and Zhao, Y., 2024. Enhancing Real-World Complex Network Representations with Hyperedge Augmentation. CoRR, v. abs/2402.13033
Jamasb, AR., Morehead, A., Joshi, CK., Zhang, Z., Didi, K., Mathis, S., Harris, C., Tang, J., Cheng, J., Liò, P. and Blundell, TL., 2024. Evaluating Representation Learning on the Protein Structure Universe. ArXiv,
Zhu, M., Bazaga, A. and Liò, P., 2024. FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models. CoRR, v. abs/2406.04501
Nobel, SMN., Swapno, SMMR., Islam, MB., Azad, AKM., Alyami, SA., Alamin, M., Liò, P. and Moni, MA., 2024. A Novel Mixed Convolution Transformer Model for the Fast and Accurate Diagnosis of Glioma Subtypes Advanced Intelligent Systems,
Doi: http://doi.org/10.1002/aisy.202400566
Bazaga, A., Liò, P. and Micklem, G., 2024. HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024,
Doi: 10.18653/v1/2024.findings-emnlp.537
Zuo, K., Jiang, Y., Mo, F. and Lio, P., 2024. KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis. CoRR, v. abs/2412.16833
Ceccarelli, F., Liò, P. and Holden, SB., 2024. AnnoGCD: a generalized category discovery framework for automatic cell type annotation. NAR Genom Bioinform, v. 6
Doi: http://doi.org/10.1093/nargab/lqae166
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: 10.1016/j.eswa.2024.123922
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Lió, P., 2024. Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting. Nat Commun, v. 15
Doi: 10.1038/s41467-024-45566-8
Zhou, B., Zheng, L., Wu, B., Yi, K., Zhong, B., Tan, Y., Liu, Q., Liò, P. and Hong, L., 2024. A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity. Cell Discov, v. 10
Doi: 10.1038/s41421-024-00728-2
Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2024. GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data Transactions on Machine Learning Research, v. 2024
Mumenin, N., Yousuf, MA., Nashiry, MA., Azad, AKM., Alyami, SA., Lio', P. and Moni, MA., 2024. ASDNet: A robust involution-based architecture for diagnosis of autism spectrum disorder utilising eye-tracking technology IET Computer Vision, v. 18
Doi: 10.1049/cvi2.12271
Bi, X., Yang, Z., Liu, B., Cun, X., Pun, C-M., Lio, P. and Xiao, B., 2024. ZeroPur: Succinct Training-Free Adversarial Purification. CoRR, v. abs/2406.03143
Oliva, V., Martone, A., Fanelli, G., Domschke, K., Minelli, A., Gennarelli, M., Martini, P., Bortolomasi, M., Maron, E., Squassina, A., Pisanu, C., Kasper, S., Zohar, J., Souery, D., Montgomery, S., Albani, D., Forloni, G., Ferentinos, P., Rujescu, D., Mendlewicz, J., De Ronchi, D., Baune, BT., Potier, MC., van Westrhenen, R., Rybakowski, F., Mehta, D., Dierssen, M., Janzing, JGE., Liò, P., Serretti, A. and Fabbri, C., 2024. Polygenic scores of subcortical brain volumes as possible modulators of treatment response in depression Neuroscience Applied, v. 3
Doi: 10.1016/j.nsa.2024.103937
Cao, P. and Lio, P., 2024. GenRec: Generative Personalized Sequential Recommendation. CoRR, v. abs/2407.21191
Yi, K., Fan, Y., Hamann, J., Liò, P. and Wang, YG., 2024. ABCMB: deep delensing assisted likelihood-free inference from CMB polarization maps Machine Learning: Science and Technology, v. 5
Doi: http://doi.org/10.1088/2632-2153/ad9af9
Bazaga, A., Liò, P. and Micklem, G., 2024. UNSUPERVISED PRETRAINING FOR FACT VERIFICATION BY LANGUAGE MODEL DISTILLATION 12th International Conference on Learning Representations, ICLR 2024,
Moss, J., England, J. and Lió, P., 2024. Deep Kernel Learning of Nonlinear Latent Force Models Transactions on Machine Learning Research, v. 2024
Luca, VMD., Longa, A., Passerini, A. and Liò, P., 2024. xAI-Drop: Don't Use What You Cannot Explain. CoRR, v. abs/2407.20067
Ceccarelli, F., Liò, P. and Holden, SB., 2024. AnnoGCD: a generalized category discovery framework for automatic cell type annotation NAR Genomics and Bioinformatics, v. 6
Doi: http://doi.org/10.1093/nargab/lqae166
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: 10.1609/aaai.v37i8.26090
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
Yang, J. and Liò, P., 2023. Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction. CoRR, v. abs/2312.00677
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: 10.1016/j.lfs.2023.122244
Georgiev, D., Numeroso, D., Bacciu, D. and Liò, P., 2023. Neural Algorithmic Reasoning for Combinatorial Optimisation Proceedings of Machine Learning Research, v. 231
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: 10.1016/j.engappai.2022.105683
Kazhdan, D., Dimanov, B., Magister, LC., Barbiero, P., Jamnik, M. and Liò, P., 2023. GCI: A (G)raph (C)oncept (I)nterpretation Framework. CoRR, v. abs/2302.04899
Duval, A., Mathis, SV., Joshi, CK., Schmidt, V., Miret, S., Malliaros, FD., Cohen, T., Lio, P., Bengio, Y. and Bronstein, MM., 2023. A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. CoRR, v. abs/2312.07511
Azzolin, S., Longa, A., Barbiero, P., Liò, P. and Passerini, A., 2023. GLOBAL EXPLAINABILITY OF GNNS VIA LOGIC COMBINATION OF LEARNED CONCEPTS 11th International Conference on Learning Representations, ICLR 2023,
Shen, Z., Cheng, Y., Chan, RH., Liò, P., Schönlieb, C-B. and Avilés-Rivero, AI., 2023. TRIDENT: The Nonlinear Trilogy for Implicit Neural Representations. CoRR, v. abs/2311.13610
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Liò, P., 2023. Modelling local and general quantum mechanical properties with attention-based pooling. Commun Chem, v. 6
Doi: 10.1038/s42004-023-01045-7
Joshi, CK., Jamasb, AR., Viñas, R., Harris, C., Mathis, SV. and Liò, P., 2023. Multi-State RNA Design with Geometric Multi-Graph Neural Networks. CoRR, v. abs/2305.14749
Crisostomi, D., Cannistraci, I., Moschella, L., Barbiero, P., Ciccone, M., Liò, P. and Rodolà, E., 2023. From Charts to Atlas: Merging Latent Spaces into One. CoRR, v. abs/2311.06547
Didi, K., Vargas, F., Mathis, SV., Dutordoir, V., Mathieu, E., Komorowska, UJ. and Lio, P., 2023. A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. CoRR, v. abs/2312.09236
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
Doi: 10.1609/aaai.v37i9.26267
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: 10.1109/TPDS.2023.3317388
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
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
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Conference proceedings
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Huang, K., Cao, W., Ta, H., Xiao, X. and Liò, P., 2024. Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach WWW 2024 - Proceedings of the ACM Web Conference,
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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
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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,
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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,
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Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and Schaar, MVD., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. AISTATS,
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Duta, I., Cassarà, G., Silvestri, F. and Lió, P., 2023. Sheaf Hypergraph Networks. NeurIPS,
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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,
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Borde, HSDO., Kazi, A., Barbero, F. and Liò, P., 2023. Latent Graph Inference using Product Manifolds. ICLR,
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Sun, Z., Cristea, AI., Lio, P. and Yu, J., 2023. Adaptive Distance Message Passing From the Multi-Relational Edge View. Tiny Papers @ ICLR,
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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
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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
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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,
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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
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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
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Imrie, F., Norcliffe, A., Lió, P. and Schaar, MVD., 2022. Composite Feature Selection Using Deep Ensembles. NeurIPS,
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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,
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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
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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,
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
Lio, P., 2011 (No publication date). Long Range Properties of DNA Sequences Collana Franco Angeli Editore,
Lio, P., Emanuela Merelli, and Nicola Paoletti, NP., 2011. Multiple verification in computational modeling of bone pathologies EPTCS, v. 67
Merelli, E., Paoletti, N. and Lio, P., 2011. Methodological Bridges for Multi-Level Systems Procedia Computer Science, v. 7
2011. Artificial Immune Systems - 10th International Conference, ICARIS 2011, Cambridge, UK, July 18-21, 2011. Proceedings ICARIS, v. 6825
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: 10.1016/j.jbiotec.2010.09.846
Ostilli, M., Yoneki, E., Leung, IXY., Mendes, JFF., Lio, P. and Crowcroft, J., 2010. Statistical mechanics of rumour spreading in network communities ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS, v. 1
Doi: 10.1016/j.procs.2010.04.262
Papini, A., Mosti, S., Lio, P. and Haider, S., 2010. BIOLIP, a biotechnology-oriented database of oil content in plants, algae, fungi and cyanobacteria JOURNAL OF BIOTECHNOLOGY, v. 150
Doi: 10.1016/j.jbiotec.2010.09.012
Kitchovitch, S. and Lio, P., 2010. Risk perception and disease spread on social networks ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS, v. 1
Doi: 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: 10.1016/j.procs.2010.04.265
Bartoszek, K., Lio, P. and Sorathiya, A., 2010. INFLUENZA DIFFERENTIATION AND EVOLUTION SUMMER SOLSTICE 2009 INTERNATIONAL CONFERENCE ON DISCRETE MODELS OF COMPLEX SYSTEMS, v. 3
Aldinucci, M., Bracciali, A., Liò, P., Sorathiya, A. and Torquati, M., 2010. StochKit-FF: Efficient Systems Biology on Multicore Architectures. Euro-Par Workshops, v. 6586
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
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)
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: 10.1186/1471-2164-10-S3-S14
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., Cheng, TMK., Dunbar, R., Liò, P. and Crowcroft, J., 2009. On optimising personal network size to manage information flow. CIKM-CNIKM,
Lu, Y-E., Roberts, SGB., Liò, P., Dunbar, R. and Crowcroft, J., 2009. Size Matters: Variation in Personal Network Size, Personality and Effect on Information Transmission. CSE (4),
Doi: 10.1109/CSE.2009.179
Xie, SK., Lio, P. and Lawniczak, AT., 2009. A Comparative Study of Noise Effect on Wavelet Based De-noising Methods IEEE TIC-STH 09: 2009 IEEE TORONTO INTERNATIONAL CONFERENCE: SCIENCE AND TECHNOLOGY FOR HUMANITY,
Xu, K., Hui, P., Li, VOK., Crowcroft, J., Latora, V. and Lio, P., 2009. Impact of Altruism on Opportunistic Communications 2009 FIRST INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS,
Kitchovitch, S., Song, YD., van der Wath, R. and Lio, P., 2009. Substitution Matrices and Mutual Information Approaches to Modeling Evolution LEARNING AND INTELLIGENT OPTIMIZATION, v. 5851
Kitchovitch, S., Leung, I., Song, YD. and Lio, P., 2009. Using Mutual Information and Models of Evolution for improved pattern detection 2009 INTERNATIONAL JOINT CONFERENCE ON BIOINFORMATICS, SYSTEMS BIOLOGY AND INTELLIGENT COMPUTING, PROCEEDINGS,
Doi: 10.1109/IJCBS.2009.77
Bagnoli, F., Guazzini, A. and Lio, P., 2008. Human Heuristics for Autonomous Agents BIO-INSPIRED COMPUTING AND COMMUNICATION, v. 5151
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
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,
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
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
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,
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
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
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
van der Wath, RC., van der Wath, E., Carapelli, A., Nardi, F., Frati, F., Milanesi, L. and Lio, P., 2008. Bayesian phylogeny on grid BIOINFORMATICS RESEARCH AND DEVELOPMENT, PROCEEDINGS, v. 13
Xie, SK., Lawniczak, AT. and Lio, P., 2008. Parametric & non-parametric analysis of mean treatment effects of number of packets in transit in data network model 2008 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-4,
2008. Bio-Inspired Computing and Communication, First Workshop on Bio-Inspired Design of Networks, BIOWIRE 2007, Cambridge, UK, April 2-5, 2007, Revised Selected Papers BIOWIRE, v. 5151
Lu, XF., Wicker, F., Lio', P. and Towsley, D., 2008. Security Estimation Model with Directional Antennas 2008 IEEE MILITARY COMMUNICATIONS CONFERENCE: MILCOM 2008, VOLS 1-7,
Allen, SM., Conti, M., Crowcroft, J., Dunbar, R., Liò, P., Mendes, JFF., Molva, R., Passarella, A., Stavrakakis, I. and Whitaker, RM., 2008. Social Networking for Pervasive Adaptation. SASO Workshops,
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,
Kershenbaum, A., Pappas, V., Lee, KW., Lio, P., Sadler, B. and Verma, D., 2008. A Biologically-Inspired MANET Architecture Proceedings of SPIE, the International Society for Optical Engineering,
Lu, YE., Lio, P. and Hand, S., 2007. Beta random projection ISM WORKSHOPS 2007: NINTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA - WORKSHOPS, PROCEEDINGS,
Doi: 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: 10.1186/1471-2148-7-S2-S5
Milanesi, L., Lio, P. and Breton, V., 2007. Bioinformatics Challenges in Life Science IST-Africa 2007 Conference Proceedings, Paul Cunningham and Miriam Cunningham (Eds), IIMC International Information Management Corporation, 2007, ISBN: 1-905824-04-1,
Angelini, C., Cutillo, L., De Feis, I., Van der Wath, R. and Lio, P., 2007. Identifying regulatory sites using neighborhood species Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedings, v. 4447
Lawniczak, AT., Lio, P., Xie, S. and Xu, JY., 2007. Wavelet spectral analysis of packet traffic near phase transition point from free flow to congestion in data network model 2007 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3,
Fani, R., Brilli, M., Fondi, M. and Lio, P., 2007. The role of gene fusions in the evolution of metabolic pathways: the histidine biosynthesis case BMC EVOLUTIONARY BIOLOGY, v. 7
Doi: 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: 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
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. The influence of risk perception in epidemics: A cellular agent model CELLULAR AUTOMATA, PROCEEDINGS, v. 4173
Sguanci, L., Lio, P. and Bagnoli, F., 2006. Modeling evolutionary dynamics of HIV infection COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 4210
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: 10.1016/j.gene.2004.11.033
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
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: 10.1098/rstb.1999.0367
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,
Bagnoli, F., Guasti, G. and Lio, P., 1995. Translation optimization in bacteria: Statistical models NONLINEAR EXCITATIONS IN BIOMOLECULES,
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),
Theses / dissertations
Scherer, P., 2024 (No publication date). Distributional and relational inductive biases for graph representation learning in biomedicine
Doi: 10.17863/CAM.107338
Bernstein, A., 2023 (No publication date). Immune Infiltrates in Breast Cancer: Clinical Significance from Histopathology to Prognosis
Doi: 10.17863/CAM.99938
Zhu, J., 2023 (No publication date). Deep neural networks for medical image super-resolution
Doi: 10.17863/CAM.101535
Tilly, T., 2023 (No publication date). Deep learning of regulatory sequence variation in Pulmonary Arterial Hypertension
Doi: 10.17863/CAM.100199
Bodnar, C., 2023 (No publication date). Topological Deep Learning: Graphs, Complexes, Sheaves
Doi: 10.17863/CAM.97212
Christensen, CN., 2023 (No publication date). Deep learning for image processing in optical super-resolution microscopy
Doi: 10.17863/CAM.101919
Rocheteau, E., 2023 (No publication date). Representation Learning for Patients in the Intensive Care Unit
Doi: 10.17863/CAM.96504
Deasy, J., 2022 (No publication date). Relaxing assumptions in deep probabilistic modelling
Doi: 10.17863/CAM.90966
Azevedo, T., 2022 (No publication date). Data-driven Representations in Brain Science: Modelling Approaches in Gene Expression and Neuroimaging Domains
Doi: 10.17863/CAM.86924
Spivakovsky-Gonzalez, P., 2022 (No publication date). Computational Tools for Metabolic Modeling and Gene Duplication Analysis
Doi: 10.17863/CAM.86478
Spasov, S., 2022 (No publication date). Encoding parameter and structural efficiency in deep learning
Doi: 10.17863/CAM.84834
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
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.
Doi: 10.17863/CAM.54136
Book chapters
Giannini, F., Fioravanti, S., Barbiero, P., Tonda, A., Liò, P. and Di Lavore, E., 2024. Categorical Foundation of Explainable AI: A Unifying Theory
Doi: 10.1007/978-3-031-63800-8_10
Ciravegna, G., Giannini, F., Barbiero, P., Gori, M., Lio, P., Maggini, M. and Melacci, S., 2023. Learning logic explanations by neural networks
Doi: 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: 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: 10.1002/9781394229086.ch6
Rocheteau, E., Bica, I., Liò, P. and Ercole, A., 2023. Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients
Doi: 10.1007/978-3-031-36938-4_6
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: 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: 10.3233/faia230157
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: 10.1142/9781800610941_0005
Vijayakumar, S., Conway, M., Lió, P. and Angione, C., 2018. Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives.
Doi: 10.1007/978-1-4939-7528-0_18
Felicetti, L., Femminella, M., Liò, P. and Reali, G., 2017. Effect of aging, disease versus health conditions in the design of nano-communications in blood vessels
Doi: 10.1007/978-3-319-50688-3_19
Di Stefano, A., La Corte, A., Lió, P. and Scatá, M., 2016. Bio-Inspired ICT for Big Data Management in Healthcare
Doi: 10.1007/978-3-319-23742-8_1
Liu, Z., Tang, L. and Yan, J., 2015. A random early detection based active queue management algorithm in power optical communication network
Doi: 10.1201/b18592-52
Lio, P., Bianchi, L., Nguyen, V. and Kitchovich, S., 2013. Risk Perception, Heuristics and Epidemic Spread
Bansal, A., Azad, S. and Lio, P., 2013. Malaria incidence forecasting and its implication to intervention strategies in South East Asia Region
Doi: 10.1007/978-3-319-00395-5_110
Lio, P. and Verma, D., 2012. Biologically Inspired Networking and Sensing: Algorithms and Architectures Preface
Brilli, M. and Lio, P., 2010. The structural and dynamical properties of biological systems
Lio, P. and Brilli, M., 2010. Transcription factors and gene regulatory networks
Emiliani, G., Fondi, M., Lio, P. and Fani, R., 2010. Evolution of Metabolic Pathways and Evolution of Genomes
Brilli, M., Fani, R. and Lio, P., 2010. Bioinformatics of gene families
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
Renato Fani, RF., Silvia Casadei, SC. and Lio, P., 2002. Origin and Evolution of nif Genes
Doi: 10.1007/0-306-47615-0_85
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
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: 10.1101/2022.04.26.489341
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
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: 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: 10.1101/801605
Books
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
Aiello, LM., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P. and Rocha, LM., 2019. Preface
Bartocci, E., Lio, P. and Paoletti, N., 2016. Preface
Di Serio, C., Liò, P., Nonis, A. and Tagliaferri, R., 2015. Preface
Lio, P. and Verma, D., 2011. Biologically Inspired Networking and Sensing
