Conference proceedings
Dong, T., Jamnik, M. and Liò, P., 2025 (Published online). Neural Reasoning for Sure Through Constructing Explainable Models Proceedings of the AAAI Conference on Artificial Intelligence, v. 39
Doi: 10.1609/aaai.v39i11.33262
Dong, T., Jamnik, M. and Liò, P., 2025. Neural Reasoning for Sure Through Constructing Explainable Models. AAAI,
Zhao, X., Li, Z., Shen, M., Stan, GB., Liò, P. and Zhao, Y., 2024. Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation Frontiers in Artificial Intelligence and Applications, v. 392
Doi: http://doi.org/10.3233/FAIA240652
Komorowska, UJ., Mathis, S., Didi, K., Vargas, F., Lio, P. and Jamnik, M., 2024. Dynamics-Informed Protein Design with Structure Conditioning
Shen, Y., Chen, Z., Mamalakis, M., Liu, Y., Li, T., Su, Y., He, J., Liò, P. and Wang, YG., 2024. TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024,
Doi: http://doi.org/10.1109/BIBM62325.2024.10822695
Bazaga, A., Lio, P. and Micklem, G., 2024. Unsupervised Pretraining for Fact Verification by Language Model Distillation. ICLR,
Papamarkou, T., Birdal, T., Bronstein, M., Carlsson, G., Curry, J., Gao, Y., Hajij, M., Kwitt, R., Liò, P., Di Lorenzo, P., Maroulas, V., Miolane, N., Nasrin, F., Ramamurthy, KN., Rieck, B., Scardapane, S., Schaub, MT., Veličković, P., Wang, B., Wang, Y., Wei, G-W. and Zamzmi, G., 2024. Position: Topological Deep Learning is the New Frontier for Relational Learning. Proc Mach Learn Res, v. 235
Kovac, V., Bekkers, EJ., Liò, P. and Eijkelboom, F., 2024. E(n) Equivariant Message Passing Cellular Networks Proceedings of Machine Learning Research, v. 251
Zaghen, O., Longa, A., Azzolin, S., Telyatnikov, L., Passerini, A. and Liò, P., 2024. Sheaf Diffusion Goes Nonlinear: Enhancing GNNs with Adaptive Sheaf Laplacians Proceedings of Machine Learning Research, v. 251
Giusti, L., Reu, T., Ceccarelli, F., Bodnar, C. and Liò, P., 2024. Topological Message Passing for Higher - Order and Long - Range Interactions Proceedings of the International Joint Conference on Neural Networks,
Doi: 10.1109/IJCNN60899.2024.10650343
Huang, K., Cao, W., Ta, H., Xiao, X. and Liò, P., 2024. Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach WWW 2024 - Proceedings of the ACM Web Conference,
Doi: 10.1145/3589334.3645705
Ceccarelli, F., Prinzi, F., Liò, P., Vitabile, S. and Holden, SB., 2024. MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRI Proceedings of the International Joint Conference on Neural Networks,
Doi: 10.1109/IJCNN60899.2024.10650117
Bazaga, A., Lio, P. and Micklem, G., 2024. HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs. EMNLP (Findings),
Bazaga, A., Lio, P. and Micklem, G., 2024. Language Model Knowledge Distillation for Efficient Question Answering in Spanish. Tiny Papers @ ICLR,
Ceccarelli, F., Prinzi, F., Liò, P., Vitabile, S. and Holden, SB., 2024. MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRI. IJCNN,
Huang, K., Wang, YG., Li, M. and Lio, P., 2024. How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing. ICML,
Denker, A., Vargas, F., Padhy, S., Didi, K., Mathis, S., Dutordoir, V., Barbano, R., Mathieu, E., Komorowska, UJ. and Lio, P., 2024. DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transform Advances in Neural Information Processing Systems, v. 37
Ceccarelli, F., Giusti, L., Holden, S. and Lio, P., 2024. Integrating Structure and Sequence: Protein Graph Embeddings via GNNs and LLMs
Doi: 10.5220/0012453600003654
Iuliano, A., Lio, P., Manfredi, G. and Romaniello, F., 2024. Denoising Probabilistic Diffusion Models for Synthetic Healthcare Image Generation 2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024 - Proceedings,
Doi: 10.1109/MetroLivEnv60384.2024.10615511
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,
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: 10.1109/CVPR52729.2023.01007
Georgiev, D., Numeroso, D., Bacciu, D. and Lio, P., 2023. Neural Algorithmic Reasoning for Combinatorial Optimisation. LoG, v. 231
Opolka, FL., Zhi, YC., Liò, P. and Dong, X., 2023. Graph Classification Gaussian Processes via Spectral Features Proceedings of Machine Learning Research, v. 216
Borde, HSDO., Kazi, A., Barbero, F. and Liò, P., 2023. Latent Graph Inference using Product Manifolds. ICLR,
Azzolin, S., Longa, A., Barbiero, P., Liò, P. and Passerini, A., 2023. Global Explainability of GNNs via Logic Combination of Learned Concepts. ICLR,
Sun, Z., Cristea, AI., Lio, P. and Yu, J., 2023. Adaptive Distance Message Passing From the Multi-Relational Edge View. Tiny Papers @ ICLR,
Keskin, O., Lupidi, A., Giannini, F., Fioravanti, S., Magister, LC., Barbiero, P. and Liò, P., 2023. Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach Proceedings of Machine Learning Research, v. 221
Lu, X., Zhang, X. and Lio, P., 2023. GAT-DNS: DNS Multivariate Time Series Prediction Model Based on Graph Attention Network ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023,
Doi: 10.1145/3543873.3587329
Bernárdez, G., Telyatnikov, L., Alarcón, E., Cabellos-Aparicio, A., Barlet-Ros, P. and Liò, P., 2023. Topological Network Traffic Compression GNNet 2023 - Proceedings of the 2nd Graph Neural Networking Workshop 2023,
Doi: 10.1145/3630049.3630172
Mittone, G., Svoboda, F., Aldinucci, M., Lane, N. and Lió, P., 2023. A Federated Learning Benchmark for Drug-Target Interaction ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023,
Doi: 10.1145/3543873.3587687
Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and van der Schaar, M., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis Proceedings of Machine Learning Research, v. 206
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: 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: 10.1109/BigData59044.2023.10386951
Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and Schaar, MVD., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. AISTATS,
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
Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2023. Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, v. 37
Doi: 10.1609/aaai.v37i8.26090
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
Duta, I., Cassarà, G., Silvestri, F. and Lió, P., 2023. Sheaf Hypergraph Networks. NeurIPS,
Zou, X., Zhao, X., Lio, P. and Zhao, Y., 2023. Will More Expressive Graph Neural Networks Do Better on Generative Tasks? LoG, v. 231
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
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: 10.1183/13993003.congress-2022.2543
Yi, K., Chen, J., Wang, YG., Zhou, B., Liò, P., Fan, Y. and Hamann, J., 2022. APPROXIMATE EQUIVARIANCE SO(3) NEEDLET CONVOLUTION Proceedings of Machine Learning Research, v. 196
Liu, L., Huang, Z., Liò, P., Schönlieb, CB. and Aviles-Rivero, AI., 2022. You only Look at Patches: A Patch-wise Framework for 3D Unsupervised Medical Image Registration Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13386 LNCS
Doi: 10.1007/978-3-031-11203-4_21
Cardozo, S., Montero, GI., Kazhdan, D., Dimanov, B., Wijaya, M., Jamnik, M. and Lio, P., 2022. Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations CEUR Workshop Proceedings, v. 3318
Day, B., Viñas, R., Simidjievski, N. and Liò, P., 2022. Attentional Meta-learners for Few-shot Polythetic Classification Proceedings of Machine Learning Research, v. 162
Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S. and Liò, P., 2022. 3D Infomax improves GNNs for Molecular Property Prediction Proceedings of Machine Learning Research, v. 162
Fan, J., Pei, J., Bi, X., Xiao, B. and Lio, P., 2022. Context Correlation Aware Network for Cardiac Segmentation Proceedings - IEEE International Conference on Multimedia and Expo, v. 2022-July
Doi: 10.1109/ICME52920.2022.9859985
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Liò, P., 2022. Graph Neural Networks with Adaptive Readouts. NeurIPS,
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: 10.1109/DSN-W54100.2022.00010
Manouchehrinia, A., Ebrahimi, A., Wiil, UK., Kiani, NA., Lio, P., Olsson, T. and Kockum, I., 2022. A susceptibility network analysis of disease pathways leading to multiple sclerosis MULTIPLE SCLEROSIS JOURNAL, v. 28
Zarlenga, ME., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Lio, P. and Jamnik, M., 2022. Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off Advances in Neural Information Processing Systems, v. 35
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: 10.1007/978-3-031-12053-4_5
Opolka, FL. and Liò, P., 2022. Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes Proceedings of Machine Learning Research, v. 151
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
Doi: 10.1609/aaai.v36i6.20551
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
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
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
Doi: 10.1609/aaai.v36i6.20623
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,
Doi: 10.18653/v1/2022.emnlp-main.604
Pándy, M., Qiu, W., Corso, G., Veličković, P., Ying, R., Leskovec, J. and Liò, P., 2022. Learning Graph Search Heuristics Proceedings of Machine Learning Research, v. 198
Tailor, SA., Opolka, FL., Liò, P. and Lane, ND., 2022. DO WE NEED ANISOTROPIC GRAPH NEURAL NETWORKS? ICLR 2022 - 10th International Conference on Learning Representations,
Lu, X., Zhao, J. and Lio, P., 2022. Robust android malware detection based on subgraph network and denoising GCN network MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services,
Doi: 10.1145/3498361.3538778
He, Y., Veličković, P., Liò, P. and Deac, A., 2022. Continuous Neural Algorithmic Planners Proceedings of Machine Learning Research, v. 198
Aghakhanyan, G., Barucci, A., Colantonio, S., Colcelli, V., Pasquinelli, F., Gini, R., Lio, P., Mazzei, M., Erba, P., Miele, V. and Neri, E., 2022. NAVIGATOR: An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, v. 49
Imrie, F., Norcliffe, A., Lió, P. and Schaar, MVD., 2022. Composite Feature Selection Using Deep Ensembles. NeurIPS,
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
Buffelli, D., Lió, P. and Vandin, F., 2022. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. NeurIPS,
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: 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
Bodnar, C., Giovanni, FD., Chamberlain, BP., Lió, P. and Bronstein, MM., 2022. Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. NeurIPS,
Opolka, FL., Zhi, Y-C., Liò, P. and Dong, X., 2022. Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets. AISTATS, v. 151
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: 10.1109/ICASSP43922.2022.9746330
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Liò, P., 2022. Graph Neural Networks with Adaptive Readouts Advances in Neural Information Processing Systems, v. 35
Scherer, P., Liò, P. and Jamnik, M., 2022. Distributed Representations of Graphs for Drug Pair Scoring Proceedings of Machine Learning Research, v. 198
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
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
Norcliffe, A., Bodnar, C., Day, B., Moss, J. and Liò, P., 2021. Neural ODE Processes
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: 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. 22
Kazhdan, D., Dimanov, B., Terre, HA., Jamnik, M., Liò, P. and Weller, A., 2021. Is Disentanglement all you need? Comparing Concept-based &
Disentanglement Approaches
Qendro, L., Campbell, A., Liò, P. and Mascolo, C., 2021. Early Exit Ensembles for Uncertainty Quantification Proceedings of Machine Learning Research, v. 158
Drotár, P., Jamasb, AR., Day, B., Cangea, C. and Liò, P., 2021. Structure-aware generation of drug-like molecules. CoRR, v. abs/2111.04107
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: 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
Moss, JD., Opolka, FL., Dumitrascu, B. and Lió, P., 2021. Approximate Latent Force Model Inference
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: 10.1109/MLSP52302.2021.9690626
Wei, X., Pu, C., He, Z. and Lio, P., 2021. Deep Reinforcement Learning-based Vaccine Distribution Strategies Proceedings - 2021 2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021,
Doi: 10.1109/CECIT53797.2021.00082
Bodnar, C., Frasca, F., Wang, YG., Otter, N., Montúfar, G., Liò, P. and Bronstein, MM., 2021. Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Proceedings of Machine Learning Research, v. 139
Lu, X. and Lio, P., 2021. International Workshop on Application of Intelligent Technology in Security - AITS 2021 Proceedings - 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2021,
Doi: 10.1109/DSN-W52860.2021.00008
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: 10.1109/IJCNN48605.2020.9207564
Bardozzo, F., Lio', P. and Tagliaferri, R., 2020 (No publication date). A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
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: 10.1109/EMBC44109.2020.9176613
Kazhdan, D., Dimanov, B., Jamnik, M., Liò, P. and Weller, A., 2020. Now you see me (CME): Concept-based model extraction CEUR Workshop Proceedings, v. 2699
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,
Bodnar, C., Day, B. and Lió, P., 2020. Proximal Distilled Evolutionary Reinforcement Learning. AAAI,
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: 10.1007/978-3-030-63061-4_10
Wang, D., Jamnik, M. and Lio, P., 2020. Abstract Diagrammatic Reasoning with Multiplex Graph Networks
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: 10.1007/978-3-030-34585-3_19
Deasy, J., Ercole, A. and Liò, P., 2020. Adaptive Prediction Timing for Electronic Health Records
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: 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 ACM International Conference Proceeding Series, v. Part F165625
Doi: 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: 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: 10.1109/EMBC44109.2020.9175360
Yeghikyan, G., Opolka, FL., Nanni, M., Lepri, B. and Lio, P., 2020. Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks**To appear in the Proceedings of 2020 IEEE International Conference on Smart Computing (SMARTCOMP 2020) Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020,
Doi: 10.1109/SMARTCOMP50058.2020.00028
Veličković, P., Fedus, W., Hamilton, WL., Liò, P., Bengio, Y. and Hjelm, RD., 2019 (No publication date). Deep Graph Infomax
Opolka, FL., Solomon, A., Cangea, C., Veličković, P., Liò, P. and Hjelm, RD., 2019 (No publication date). Spatio-Temporal Deep Graph Infomax
Zhu, J., Yang, G. and Lió, P., 2019. Lesion focused super-resolution. 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: 10.1145/3313294.3313384
Zhu, J., Yang, G. and Lio, P., 2019. How can we make gan perform better in single medical image super-resolution? A lesion focused multi-scale approach Proceedings - International Symposium on Biomedical Imaging, v. 2019-April
Doi: 10.1109/ISBI.2019.8759517
Cangea, C., Belilovsky, E., Liò, P. and Courville, A., 2019. VideoNavQA: Bridging the Gap between Visual and Embodied Question
Answering
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,
Doi: 10.1609/aaai.v33i01.33016900
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Bagnoli, F., Liò, P. and Sguanci, L., 2007. Risk perception in epidemic modeling. Phys Rev E Stat Nonlin Soft Matter Phys, v. 76
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Liò, P., 2007. Topological and dynamical properties of genetic and social networks PAMM, v. 7
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Lawniczak, AT., Xie, S., Liò, PP. and Xu, J., 2007. Study of packet traffic fluctuations near phase transition point from free flow to congestion in data network model Canadian Conference on Electrical and Computer Engineering,
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Bianchi, L. and Lio, P., 2007. Forensic DNA and bioinformatics BRIEF BIOINFORM, v. 8
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Weston, EM., Friday, AE. and Liò, P., 2007. Biometric evidence that sexual selection has shaped the hominin face. PloS one, v. 2
Chen, F., Archambault, V., Kar, A., Lio, P., D'Avino, PP., Sinka, R., Lilley, K., Laue, ED., Deak, P., Capalbo, L. and Glover, DM., 2007. Multiple protein phosphatases are required for mitosis in Drosophila CURR BIOL, v. 17
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Papetti, C., Lio, P., Ruber, L., Patarnello, T. and Zardoya, R., 2007. Antarctic fish mitochondrial genomes lack ND6 gene J MOL EVOL, v. 65
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Weston, EM., Friday, AE. and Liò, P., 2007. Biometric evidence that sexual selection has shaped the hominin face. PLoS One, v. 2
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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
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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
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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
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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: 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
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Fani, R., Brilli, M. and Liò, P., 2006. Inference from proteobacterial operons shows piecewise organization: a reply to Price et al. J Mol Evol, v. 63
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Fani, R., Caramelli, D. and Liò, P., 2006. From prebiotic chemistry to the evolution of man: The First Conference of the S.I.B.E. (Italian Society of Evolutionary Biology) in Florence Rivista di Biologia - Biology Forum, v. 99
Fani, R., Caramelli, D. and Liò, P., 2006. 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
Sguanci, L., Bagnoli, F. and Lio, P., 2006. Mathematical Model of HIV superinfection dynamics and R5 to X4 switch
Fani, R., Brilli, M. and Lio, P., 2005. The origin and evolution of operons: The piecewise building of the proteobacterial histidine operon J MOL EVOL, v. 60
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Piazza, F. and Lio, P., 2005. Statistical analysis of simple repeats in the human genome PHYSICA A, v. 347
Doi: 10.1016/j.physa.2004.08.038
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 Nature Genetics, v. 36
Doi: 10.1038/ng1377
Tadesse, MG., Vannucci, M. and Lio, P., 2004. Identification of DNA regulatory motifs using Bayesian variable selection BIOINFORMATICS, v. 20
Doi: 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: 10.1038/ng1377
Lio, P. and Goldman, N., 2004. Phylogenomics and bioinformatics of SARS-CoV TRENDS MICROBIOL, v. 12
Liò, P., 2003. Dimensionality and dependence problems in statistical genomics. Brief Bioinform, v. 4
Doi: 10.1093/bib/4.2.168
Lio, P., 2003. Il genoma della Sars Le Scienze Italian Edition of Scientific American, v. June 2003
Lio, P., 2003. Wavelets in bioinformatics and computational biology: state of art and perspectives BIOINFORMATICS, v. 19
Lio, P., 2003. Statistical bioinformatic methods in microbial genome analysis BIOESSAYS, v. 25
Doi: 10.1002/bies.10231
Lio, P., 2002. Una vita per le proteine Le Scienze Italian Edition of Scientific American, v. February 2002
Skaer, N., Pistillo, D., Gibert, JM., Lio, P., Wulbeck, C. and Simpson, P., 2002. Gene duplication at the achaete-scute complex and morphological complexity of the peripheral nervous system in Diptera TRENDS GENET, v. 18
Liò, P. and Goldman, N., 2002. Modeling mitochondrial protein evolution using structural information. J Mol Evol, v. 54
Doi: 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. and Goldman, N., 2002. Modeling mitochondrial protein evolution using structural information J MOL EVOL, v. 54
Doi: 10.1007/s00239001-0052-7
Whelan, S., Lio, P. and Goldman, N., 2001. Molecular phylogenetics: state-of-the-art methods for looking into the past TRENDS GENET, v. 17
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
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
Bogani, P., Simoni, A., Lio, P., Germinario, A. and Buiatti, M., 2001. Molecular variation in plant cell populations evolving in vitro in different physiological contexts. Genome, v. 44
Lio, P., 2000. Siamo uomini non DNA robot,
Lio, P. and Vannucci, M., 2000. Finding pathogenicity islands and gene transfer events in genome data BIOINFORMATICS, v. 16
Thomas, NS., Wilkinson, J., Lio, P., Doull, I., Morton, NE. and Holgate, ST., 2000. Investigation of the genetic factors underlying asthma and atopy in outbred UK populations Revue des Maladies Respiratoires, v. 17
Fani, R., Gallo, R. and Liò, P., 2000. Molecular evolution of nitrogen fixation: the evolutionary history of the nifD, nifK, nifE, and nifN genes. J Mol Evol, v. 51
Doi: 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
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: 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: 10.1098/rspb.1999.0663
Liò, P. and Goldman, N., 1998. Models of molecular evolution and phylogeny. Genome Res, v. 8
Doi: 10.1101/gr.8.12.1233
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: 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: 10.1093/bioinformatics/14.8.726
Liò, P. and Goldman, N., 1998. Review: Models of molecular evolution and phylogeny Genome Research, v. 8
Doi: 10.1101/gr.8.12.1233
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: 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: 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: 10.1016/s0091-6749(97)70234-3
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: 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
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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
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., Bianchi, L., Nguyen, V. and Kitchovich, S., 2013. Risk Perception, Heuristics and Epidemic Spread
Lio, P. and Verma, D., 2012. Biologically Inspired Networking and Sensing: Algorithms and Architectures Preface
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
Brilli, M. and Lio, P., 2010. The structural and dynamical properties of biological systems
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
