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Department of Computer Science and Technology

  • Professor of Computational Biology

I am Full Professor at the department of Computer Science and Technology of the University of Cambridge and I am a member of the Artificial Intelligence group. I am a member of the Cambridge Centre for AI in Medicine.

Background: PhD in Complex Systems and Non Linear Dynamics (School of Informatics, dept of Engineering of the University of Firenze, Italy) and PhD in (Theoretical) Genetics (University of Pavia, Italy). More information is on my personal homepage

Other Affliations: Fellow and member of the Council of Clare Hall College, member of Ellis, the European Lab for Learning & Intelligent Systems, I am member of the Academia Europaea; I am listed in www.topitalianscientists.org/Top_italian_scientists_VIA-Academy.aspx

I am happy to receive enquiries for PhD applications. I have successfully completed the equality and diversity essentials.

Committees : Student complaints; Postdoc mentoring

 

Research

My research interest focuses on developing Artificial Intelligence and Computational Biology models to understand diseases complexity and address personalised and precision medicine. Current focus is on Graph Neural Network modeling to build predictive models based on the integration of multi scale, multi omics and multi physics data; integrate deep learning and mechanistic approaches; explainability and interpretability in medicine; exploiting short and long range communications in the human body, between cells and tissues and predict emerging mechanistic properties at systemic medicine level. Develop an AI-based medical digital twin to increase self-awareness; Develop an AI personal decision support system to increase social awareness.

Teaching

Current: Geometric Deep learning

Past: Bioinformatics Algorithms

Current Agenda (March):

Member of the committee to assess Pasteur Institutes (France)

Invited Talk AI and Healthcare (Padova)

 

 

 

Current and Past Postdoctoral students

  • Francesco di Giovanni
  • Tiago Azevedo
  • Helena Andres Terre
  • Nikola Simidjievski
  • Zohreh Shams
  • Alessandro di Stefano
  • Gianluca Ascolani
  • Julien Mozziconacci
  • Eric Yu-En Lu

Current and Past PhD students

  • Iulia Duta
  • Simon Mathis
  • Chaitanya Joshi
  • Alex Norcliffe
  • David Buterez
  • Dobrik Georgiev
  • Jacob Moss
  • Pietro Barbiero
  • Cristian Bodnar
  • Alexander Campbell
  • Paris Flood
  • Felix Opolka
  • Ramon Viñas Torné
  • Junwei Yang
  • Paul Scherer
  • Jacob Deasy
  • Catalina Cangea
  • Benjamin Day
  • Emma Rocheteau
  • Tiago Azevedo
  • Simeon Spasov
  • Duo Wang
  • Jin Zhu
  • Petar Velickovic
  • Helena Andres Terre
  • Giovanna maria Dimitri
  • Pablo Spivakovsky-Gonzalez
  • Thomas Brouwer
  • Maxwell Conway
  • Hui Xiao
  • Annalisa Occhipinti
  • Naruemon (Ploy) Pratanwanich
  • Yoli Shavit
  • Claudio Angione
  • Mohammad Ali Moni
  • Syed Haider
  • Stephan Kitchovitch
  • Yuedong Song
  • Ian Leung
  • Viet Anh Nguyen
  • Anilkumar Sorathiya
  • Richard Van der Wath

Publications

Conference proceedings

  • 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
  • Mittone, G., Svoboda, F., Aldinucci, M., Lane, N. and Lió, P., 2023. A Federated Learning Benchmark for Drug-Target Interaction ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023,
    Doi: http://doi.org/10.1145/3543873.3587687
  • Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and van der Schaar, M., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis Proceedings of Machine Learning Research, v. 206
  • 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
  • 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
  • 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: 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
  • 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
  • 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,
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Liò, P., 2022. Graph Neural Networks with Adaptive Readouts Advances in Neural Information Processing Systems, v. 35
  • Zhou, B., Liu, X., Liu, Y., Huang, Y., Liò, P. and Wang, YG., 2022. Well-conditioned Spectral Transforms for Dynamic Graph Representation Proceedings of Machine Learning Research, v. 198
  • 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
  • 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
  • Tilly, T., Auckland, K., Nibhani, R., Martin, J., Nihr, N., Morrell, NW., Lio', P. and Graf, S., 2022. Deep learning of regulatory regions discovers enhancer variants implicated in PAH EUROPEAN RESPIRATORY JOURNAL, v. 60
    Doi: http://doi.org/10.1183/13993003.congress-2022.2543
  • Yi, K., Chen, J., Wang, YG., Zhou, B., Liò, P., Fan, Y. and Hamann, J., 2022. APPROXIMATE EQUIVARIANCE SO(3) NEEDLET CONVOLUTION Proceedings of Machine Learning Research, v. 196
  • 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
  • Manouchehrinia, A., Ebrahimi, A., Wiil, UK., Kiani, NA., Lio, P., Olsson, T. and Kockum, I., 2022. A susceptibility network analysis of disease pathways leading to multiple sclerosis MULTIPLE SCLEROSIS JOURNAL, v. 28
  • Qian, P., Yang, J., Lió, P., Hu, P. and Qi, H., 2022. Joint Group-Wise Motion Estimation and Segmentation of Cardiac Cine MR Images Using Recurrent U-Net Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13413 LNCS
    Doi: http://doi.org/10.1007/978-3-031-12053-4_5
  • Opolka, FL. and Liò, P., 2022. Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes Proceedings of Machine Learning Research, v. 151
  • Barbiero, P., Ciravegna, G., Giannini, F., Lió, P., Gori, M. and Melacci, S., 2022. Entropy-Based Logic Explanations of Neural Networks Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, v. 36
  • Opolka, FL., Zhi, YC., Liò, P. and Dong, X., 2022. Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets Proceedings of Machine Learning Research, v. 151
  • 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
  • Corso, G., Ying, R., Pandy, M., Velickovic, P., Leskovec, J. and Lio, P., 2021. Neural Distance Embeddings for Biological Sequences ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), v. 34
  • Norcliffe, A., Bodnar, C., Day, B., Moss, J. and Liò, P., 2021. NEURAL ODE PROCESSES ICLR 2021 - 9th International Conference on Learning Representations,
  • Rocheteau, E., Liò, P. and Hyland, SL., 2021. Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit. CHIL,
  • Zubic, N. and Liò, P., 2021. An Effective Loss Function for Generating 3D Models from Single 2D Image Without Rendering. AIAI, v. 627
  • Sebenius, I., Campbell, A., Morgan, SE., Bullmore, ET. and Lio, P., 2021. Multimodal Graph Coarsening for Interpretable, MRI-Based Brain Graph Neural Network IEEE International Workshop on Machine Learning for Signal Processing, MLSP, v. 2021-January
    Doi: http://doi.org/10.1109/MLSP52302.2021.9690626
  • Wei, X., Pu, C., He, Z. and Lio, P., 2021. Deep Reinforcement Learning-based Vaccine Distribution Strategies Proceedings - 2021 2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021,
    Doi: http://doi.org/10.1109/CECIT53797.2021.00082
  • 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
  • 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
  • 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: 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: 10.1007/978-3-030-34585-3_19
  • Bodnar, C., Day, B. and Lió, P., 2020. Proximal Distilled Evolutionary Reinforcement Learning. AAAI,
  • Zhu, J., Tan, C., Yang, J., Yang, G. and Lio', P., 2020. Arbitrary Scale Super-Resolution for Brain MRI Images. International Journal of Neural Systems, v. 31
    Doi: 10.1142/S0129065721500374
  • Di Stefano, A., Maesa, DDF., Das, SK. and Liò, P., 2020. Resolution of Blockchain Conflicts through Heuristics-based Game Theory and Multilayer Network Modeling. ICDCN 2020: Proceedings of the 21st International Conference on Distributed Computing and Networking,
    Doi: http://doi.org/10.1145/3369740.3372914
  • Dimitri, GM., Beqiri, E., Placek, MM., Czosnyka, M., Ercole, A., Smielewski, P. and Lio, P., 2020. Introducing brain-heart crosstalks information in clinical decision support systems for TBI patients, through ICM+ 2020 11th Conference of the European Study Group on Cardiovascular Oscillations: Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020,
    Doi: 10.1109/ESGCO49734.2020.9158050
  • Dimitri, GM., Spasov, S., Duggento, A., Passamonti, L., Lio, P. and Toschi, N., 2020. Unsupervised stratification in neuroimaging through deep latent embeddings. Annu Int Conf IEEE Eng Med Biol Soc, v. 2020
    Doi: http://doi.org/10.1109/EMBC44109.2020.9175810
  • Azevedo, T., Passamonti, L., Lio, P. and Toschi, N., 2020. A deep spatiotemporal graph learning architecture for brain connectivity analysis. Annu Int Conf IEEE Eng Med Biol Soc, v. 2020
    Doi: http://doi.org/10.1109/EMBC44109.2020.9175360
  • Yeghikyan, G., Opolka, FL., Nanni, M., Lepri, B. and Lio, P., 2020. Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks**To appear in the Proceedings of 2020 IEEE International Conference on Smart Computing (SMARTCOMP 2020) Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020,
    Doi: 10.1109/SMARTCOMP50058.2020.00028
  • 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
  • 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,
  • 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: 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: 10.1007/978-3-030-10549-5_51
  • 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: 10.1007/978-3-030-14160-8_19
  • Spasov, SE. and Liò, P., 2019. Dynamic Neural Network Channel Execution for Efficient Training. BMVC,
  • 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: 10.1007/978-3-030-19432-1_8
  • 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,
  • 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,
  • Cangea, C., Belilovsky, E., Liò, P. and Courville, AC., 2019. VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. ViGIL@NeurIPS,
  • Zhu, J., Yang, G. and Lió, P., 2019. Lesion focused super-resolution. Medical Imaging: Image Processing, v. 10949
  • 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,
  • 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
  • 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
  • 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, 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  • 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
  • Lio, P., Angelini, C., DeFeis, I., Nguyen, V., Cutillo, L. and va der Wath, R., 2008. Statistical issues for combining replicates and nearby species data and different omics Proceedings The Art and Science of Statistical Bioinformatics The 27th Leeds Annual Statistical Research Workshop 15th - 17th July 2008,
  • Nguyen, VA., Koukolikova-Nicola, Z., Bagnoli, F. and Lio, P., 2008. Bayesian Inference on Hidden Knowledge in High-Throughput Molecular Biology Data PRICAI 2008: TRENDS IN ARTIFICIAL INTELLIGENCE, v. 5351
  • 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
  • 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., 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,
  • 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
  • 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
  • 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,
  • 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,
  • Sguanci, L., Lio, P. and Bagnoli, F., 2006. Modeling evolutionary dynamics of HIV infection COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 4210
  • Fani, R., Caramelli, D. and Lio, P., 2006. It happened... From prebiotic chemistry to human evolution Rivista di biologia,
  • Sguanci, L., Lio, P. and Bagnoli, F., 2006. The influence of risk perception in epidemics: A cellular agent model CELLULAR AUTOMATA, PROCEEDINGS, v. 4173
  • 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
  • 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
  • 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: http://doi.org/10.1098/rstb.1999.0367
  • 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,
  • Morton, NE. and Lio, P., 1997. Oligogenic linkage and map integration GENETIC MAPPING OF DISEASE GENES,
  • 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
  • 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),
  • Scata', M., Di Stefano, A., Giacchi, E., La Corte, A. and Lio, P., The Bio-Inspired and Social Evolution of Node and Data in a Multilayer Network SCITEPRESS Digital Library,
  • Bardozzo, F., Lio', P. and Tagliaferri, R., A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
  • Kazhdan, D., Dimanov, B., Jamnik, M., Lio, P. and Weller, A., Now You See Me (CME): Concept-based Model Extraction
  • Dmitry, K., Shams, Z. and Pietro, L., 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
  • Fernandes, P., Lio, P. and Milanesi, L., Challenges in building an e-health infrastructure for P5 Medicine
  • Rossi, E., Monti, F., Bronstein, M. and Liò, P., ncRNA Classification with Graph Convolutional Networks
  • Taylor, D., Spasov, S. and Liò, P., Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis and Decision Making
  • Drotár, P., Jamasb, AR., Day, B., Cangea, C. and Liò, P., Structure-aware generation of drug-like molecules
  • Veličković, P., Fedus, W., Hamilton, WL., Liò, P., Bengio, Y. and Hjelm, RD., Deep Graph Infomax
  • Webb, E., Day, B., Andres-Terre, H. and Lió, P., Factorised Neural Relational Inference for Multi-Interaction Systems
  • Prokhorov, V., Pilehvar, M., Kartsaklis, D., Lio, P. and Collier, N., Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
  • Moss, JD., Opolka, FL., Dumitrascu, B. and Lió, P., Approximate Latent Force Model Inference
  • Angione, C., Carapezza, G., Costanza, J., Lio, P. and Nicosia, G., Computing with Metabolic Machines EPiC Series in Computing,
    Doi: 10.29007/t48n
  • Margeloiu, A., Simidjievski, N., Lio, P. and Jamnik, M., Weight predictor network with feature selection for small sample tabular biomedical data
  • Deasy, J., Ercole, A. and Liò, P., Adaptive Prediction Timing for Electronic Health Records
  • Wang, D., Jamnik, M. and Lio, P., Investigating diagrammatic reasoning with deep neural networks
    Doi: 10.1007/978-3-319-91376-6_36
  • Scherer, P., Lio, P. and Jamnik, M., Distributed representations of graphs for drug pair scoring Proceedings of the First Learning on Graphs Conference (LoG 2022), v. PMLR 198
  • Opolka, FL., Solomon, A., Cangea, C., Veličković, P., Liò, P. and Hjelm, RD., Spatio-Temporal Deep Graph Infomax
  • Angione, C., Bartocci, E., Bortolussi, L., Lio, P., Occhipinti, A. and Sanguinetti, G., Bayesian Design for Whole Cell Synthetic Biology Models Proceedings of the Third International Workshop on Hybrid Systems Biology (HSB 2014),
  • Lio, P., Long Range Properties of DNA Sequences Collana Franco Angeli Editore,
  • Angione, C., Pratanwanich, N. and Lio, P., 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),
  • Nguyen, VA. and Lio, P., Filling in the gaps of biological network
  • Zhu, J., Yang, G. and Lio, P., 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),
  • Norcliffe, A., Bodnar, C., Day, B., Moss, J. and Liò, P., Neural ODE Processes
  • Cangea, C., Belilovsky, E., Liò, P. and Courville, A., VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
  • 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., Concept embedding models: Beyond the Accuracy-Explainability Trade-Off
  • Azevedo, T., Passamonti, L., Lio, P. and Toschi, N., 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
  • Journal articles

  • 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
  • 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
  • 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
  • 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
  • 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: 10.1021/acs.jcim.2c01569
  • Chowdhury, AA., Hasan Mahmud, SM., Shahjalal Hoque, KK., Ahmed, K., Bui, FM., Lio, P., Moni, MA. and Al-Zahrani, FA., 2023. StackFBAs: Detection of fetal brain abnormalities using CNN with stacking strategy from MRI images Journal of King Saud University - Computer and Information Sciences, v. 35
    Doi: http://doi.org/10.1016/j.jksuci.2023.101647
  • Charoenkwan, P., Pipattanaboon, C., Nantasenamat, C., Hasan, MM., Moni, MA., Lio', P. and Shoombuatong, W., 2023. PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning. Comput Biol Med, v. 152
    Doi: http://doi.org/10.1016/j.compbiomed.2022.106368
  • 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
  • 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
  • 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
  • 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
  • Faruqui, N., Yousuf, MA., Whaiduzzaman, M., Azad, AKM., Alyami, SA., Liò, P., Kabir, MA. and Moni, MA., 2023. SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization Electronics (Switzerland), v. 12
    Doi: 10.3390/electronics12173541
  • Petrović, A., Nikolić, M., Bugarić, U., Delibašić, B. and Lio, P., 2023. Controlling highway toll stations using deep learning, queuing theory, and differential evolution Engineering Applications of Artificial Intelligence, v. 119
    Doi: http://doi.org/10.1016/j.engappai.2022.105683
  • 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
  • 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: http://doi.org/10.1016/j.euroneuro.2023.01.001
  • 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
  • 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
  • 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
  • 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
  • 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
  • Charoenkwan, P., Chumnanpuen, P., Schaduangrat, N., Lio', P., Moni, MA. and Shoombuatong, W., 2022. Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides. J Comput Aided Mol Des, v. 36
    Doi: http://doi.org/10.1007/s10822-022-00476-z
  • Zafeiriou, S., Bronstein, M., Cohen, T., Vinyals, O., Song, L., Leskovec, J., Lio, P., Bruna, J. and Gori, M., 2022. Guest Editorial: Non-Euclidean Machine Learning IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 44
    Doi: 10.1109/TPAMI.2021.3129857
  • Dodson, J. and Lio, PA., 2022. Biologics and Small Molecule Inhibitors: an Update in Therapies for Allergic and Immunologic Skin Diseases. Curr Allergy Asthma Rep,
    Doi: http://doi.org/10.1007/s11882-022-01047-w
  • Charoenkwan, P., Schaduangrat, N., Moni, MA., Lio', P., Manavalan, B. and Shoombuatong, W., 2022. SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Comput Biol Med, v. 146
    Doi: http://doi.org/10.1016/j.compbiomed.2022.105704
  • Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Lio, P., 2022. Encoding Concepts in Graph Neural Networks
  • Aslam, AA., Baksh, RA., Pape, SE., Strydom, A., Gulliford, MC., Chan, LF. and GO-DS21 Consortium, , 2022. Diabetes and Obesity in Down Syndrome Across the Lifespan: A Retrospective Cohort Study Using U.K. Electronic Health Records. Diabetes Care, v. 45
    Doi: 10.2337/dc22-0482
  • Pisanu, C., Severino, G., De Toma, I., Dierssen, M., Fusar-Poli, P., Gennarelli, M., Lio, P., Maffioletti, E., Maron, E., Mehta, D., Minelli, A., Potier, M-C., Serretti, A., Stacey, D., van Westrhenen, R., Xicota, L., European College of Neuropsychopharmacology (ECNP) Pharmacogenomics & Transcriptomics Network, , Baune, BT. and Squassina, A., 2022. Transcriptional biomarkers of response to pharmacological treatments in severe mental disorders: A systematic review. Eur Neuropsychopharmacol, v. 55
    Doi: 10.1016/j.euroneuro.2021.12.005
  • Azevedo, T., Campbell, A., Romero-Garcia, R., Passamonti, L., Bethlehem, RAI., Liò, P. and Toschi, N., 2022. A deep graph neural network architecture for modelling spatio-temporal dynamics in resting-state functional MRI data. Med Image Anal, v. 79
    Doi: http://doi.org/10.1016/j.media.2022.102471
  • Rafidi, B., Kondapi, K., Beestrum, M., Basra, S. and Lio, P., 2022. Psychological Therapies and Mind-Body Techniques in the Management of Dermatologic Diseases: A Systematic Review. Am J Clin Dermatol, v. 23
    Doi: http://doi.org/10.1007/s40257-022-00714-y
  • Charoenkwan, P., Chiangjong, W., Nantasenamat, C., Moni, MA., Lio', P., Manavalan, B. and Shoombuatong, W., 2022. SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids. Pharmaceutics, v. 14
    Doi: 10.3390/pharmaceutics14010122
  • Barta, K., Fonacier, LS., Hart, M., Lio, P., Tullos, K., Sheary, B. and Winders, TA., 2022. Corticosteroid exposure and cumulative effects in patients with eczema: Results from a patient survey. Ann Allergy Asthma Immunol,
    Doi: 10.1016/j.anai.2022.09.031
  • Zago, E., Dal Molin, A., Dimitri, GM., Xumerle, L., Pirazzini, C., Bacalini, MG., Maturo, MG., Azevedo, T., Spasov, S., Gómez-Garre, P., Periñán, MT., Jesús, S., Baldelli, L., Sambati, L., Calandra-Buonaura, G., Garagnani, P., Provini, F., Cortelli, P., Mir, P., Trenkwalder, C., Mollenhauer, B., Franceschi, C., Liò, P., Nardini, C. and PROPAG-AGEING Consortium, , 2022. Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson's disease patients. Sci Rep, v. 12
    Doi: http://doi.org/10.1038/s41598-022-05227-6
  • Charoenkwan, P., Ahmed, S., Nantasenamat, C., Quinn, JMW., Moni, MA., Lio', P. and Shoombuatong, W., 2022. AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. Sci Rep, v. 12
    Doi: http://doi.org/10.1038/s41598-022-11897-z
  • Oykhman, P., Dookie, J., Al-Rammahy, H., de Benedetto, A., Asiniwasis, RN., LeBovidge, J., Wang, J., Ong, PY., Lio, P., Gutierrez, A., Capozza, K., Martin, SA., Frazier, W., Wheeler, K., Boguniewicz, M., Spergel, JM., Greenhawt, M., Silverberg, JI., Schneider, L. and Chu, DK., 2022. Dietary Elimination for the Treatment of Atopic Dermatitis: A Systematic Review and Meta-Analysis. J Allergy Clin Immunol Pract, v. 10
    Doi: http://doi.org/10.1016/j.jaip.2022.06.044
  • Bodnar, C., Giovanni, FD., Chamberlain, BP., Liò, P. and Bronstein, MM., 2022. Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
  • Yang, J., Küstner, T., Hu, P., Liò, P. and Qi, H., 2022. End-to-End Deep Learning of Non-rigid Groupwise Registration and Reconstruction of Dynamic MRI. Front Cardiovasc Med, v. 9
    Doi: http://doi.org/10.3389/fcvm.2022.880186
  • Coggan, H., Andres Terre, H. and Liò, P., 2022. A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth. Front Big Data, v. 5
    Doi: 10.3389/fdata.2022.941451
  • Reich, K., Lio, PA., Bissonnette, R., Alexis, AF., Lebwohl, MG., Pink, AE., Kabashima, K., Boguniewicz, M., Nowicki, RJ., Valdez, H., Zhang, F., DiBonaventura, M., Cameron, MC. and Clibborn, C., 2022. Magnitude and Time Course of Response to Abrocitinib for Moderate-to-Severe Atopic Dermatitis. J Allergy Clin Immunol Pract,
    Doi: http://doi.org/10.1016/j.jaip.2022.08.042
  • Lu, X., Xue, A., Lio, P. and Hui, P., 2022. Intelligent Decision Making Based on the Combination of Deep Reinforcement Learning and an Influence Map Applied Sciences (Switzerland), v. 12
    Doi: http://doi.org/10.3390/app122211458
  • Patel, S. and Lio, P., 2022. Efficacy, Safety, and Applications of Skin Protectants. J Drugs Dermatol, v. 21
    Doi: http://doi.org/10.36849/JDD.6705
  • Patel, S., Patel, S., Shah, RM., Shah, S., Doshi, S. and Lio, PA., 2022. Engagement in sun-protective practices based on health insurance coverage: A cross-sectional analysis. J Am Acad Dermatol,
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  • Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 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
  • Bonchak, JG. and Lio, PA., Nonpharmacologic interventions for chronic pruritus Itch, v. 5
    Doi: 10.1097/itx.0000000000000031
  • Lio, P., Physio-Environmental Sensing and Live Modeling interactive Journal of Medical Research (i-JMR), v. 2
    Doi: http://doi.org/10.2196/ijmr.2092.
  • Goddard, D., Lio, P., Kay, J. and Liu, V., Nephrogenic Fibrosing Dermopathy: A Review of the Massachusetts General Hospital Experience Journal of Cutaneous Pathology, v. 32
    Doi: 10.1111/j.0303-6987.2005.320ce.x
  • Cvejic, A., Tangherloni, A. and Liò, P., Analysis of single-cell RNA sequencing data based on autoencoders BMC Bioinformatics,
  • Rittman, T., Azevedo, T., Bethlehem, R., Whiteside, D., Swaddiwudhipong, N., Rowe, J. and Lio, P., 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., The Impact of Imputation Quality on Machine Learning Classifier Performance for Datasets with Missing Values Communications Medicine,
  • Corso, G., Cavalleri, L., Beaini, D., Liò, P. and Velickovic, P., Principal Neighbourhood Aggregation for Graph Nets Advances in Neural Information Processing Systems, v. 2020-December
  • Ercole, A., Deasy, J. and Lio, P., Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning arXiv,
  • Roberts, M., Driggs, D., Thorpe, MATTHEW., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A., Etmann, C., McCague, C., Beer, L., Weir-McCall, J., Teng, Z., Gkrania-Klotsas, E., Collaboration, AIX-COVNET., Rudd, J., Sala, E. and Schoenlieb, C-B., Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans Nature Machine Intelligence,
  • Christensen, CN., Ward, EN., Lio, P. and Kaminski, CF., ML-SIM: A deep neural network for reconstruction of structured illumination microscopy images arXiv,
  • Bilimoria, S., Lee, K. and Lio, P., Evaluating the predictive utility of patient‐oriented scoring of atopic dermatitis (PO‐SCORAD) versus Patient‐Oriented Eczema Measure (POEM) for peanut sensitivity in patients with atopic dermatitis JEADV Clinical Practice,
    Doi: http://doi.org/10.1002/jvc2.69
  • Norcliffe, A., Bodnar, C., Day, B., Simidjievski, N. and Liò, P., On Second Order Behaviour in Augmented Neural ODEs Advances in Neural Information Processing Systems, v. 2020-December
  • Maj, C., Azevedo, T., Giansanti, V., Borisov, O., Dimitri, GM., Spasov, S., Alzheimer’s Disease Neuroimaging Initiative, , Lió, P. and Merelli, I., 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
  • Book chapters

  • 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: 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
  • 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., 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
  • 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
  • Zafar, F. and Lio, P., 2021. Complementary and Alternative Medicine and Dermatooncology
    Doi: 10.1007/978-3-030-53437-0_12
  • 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
  • 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
  • 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
  • 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
  • Wilmer, E., Lee, K. and Lio, P., 2014. Integrative Management of Urticaria
    Doi: 10.1093/med/9780199907922.003.0027
  • Jacobson, R., Lee, K. and Lio, P., 2014. Integrative Management of Seborrheic Dermatitis
    Doi: 10.1093/med/9780199907922.003.0024
  • Lee, K. and Lio, P., 2014. Integrative Management of Atopic Dermatitis
    Doi: 10.1093/med/9780199907922.003.0014
  • Lee, K. and Lio, P., 2014. Traditional Chinese Medicine and Acupuncture in Dermatology
    Doi: 10.1093/med/9780199907922.003.0007
  • 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
  • Brilli, M. and Lio, P., 2010. The structural and dynamical properties of biological systems
  • 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
  • Lio, P. and Brilli, M., 2010. Transcription factors and gene regulatory networks
  • Liò, P., Brilli, M. and Fani, R., 2007. Phylogenetics and Computational Biology of Multigene Families
    Doi: 10.1007/978-3-540-35306-5_9
  • Carapelli, A., Nardi, F., Dallai, R., Boore, JL., Lio, P. and Frati, F., 2005. Relationships between hexapods and crustaceans based on 4 mitochondrial genes
  • Carapelli, A., Nardi, F., Dallai, R., Boore, J., LiÒ, P. and Frati, F., 2005. Relationships between hexapods and crustaceans based on four mitochondrial genes
    Doi: 10.1201/9781420037548.ch12
  • 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
  • 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
  • Li��, P. and Bishop, MJ., Nucleic Acid and Protein Sequence Analysis and Bioinformatics
    Doi: 10.1002/3527600906.mcb.200400067
  • 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
  • Banerjee, S., Liò, P., Jones, P. and Cardinal, R., 2021. A human-interpretable machine learning approach to predict mortality in severe mental illness
    Doi: http://doi.org/10.1101/2021.04.05.21254684
  • Wang, D., Jamnik, M. and Lio, P., 2020. Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds
  • Zhao, Y., Wang, D., Gao, X., Mullins, R., Lio, P. and Jamnik, M., 2020. Probabilistic Dual Network Architecture Search on Graphs
  • 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
  • Luzhnica, E., Day, B. and Liò, P., 2019. On Graph Classification Networks, Datasets and Baselines
  • Books

  • 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
  • Lio, P. and Zuliani, P., 2019. Automated Reasoning for Systems Biology and Medicine 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
  • Theses / dissertations

  • Zhu, J., Deep neural networks for medical image super-resolution
  • Tilly, T., Deep learning of regulatory sequence variation in Pulmonary Arterial Hypertension
  • Azevedo, T., Data-driven Representations in Brain Science: Modelling Approaches in Gene Expression and Neuroimaging Domains
  • Bodnar, C., Topological Deep Learning: Graphs, Complexes, Sheaves
  • Christensen, CN., Deep learning for image processing in optical super-resolution microscopy
  • Spivakovsky-Gonzalez, P., Computational Tools for Metabolic Modeling and Gene Duplication Analysis
  • Wang, D., Neural Diagrammatic Reasoning
  • Spasov, S., Encoding parameter and structural efficiency in deep learning
  • Dimanov, B., Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations
  • Andres Terre, H., Interpreting Deep Learning for cell differentiation. Supervised and Unsupervised models viewed through the lens of information and perturbation theory.
  • Rocheteau, E., Representation Learning for Patients in the Intensive Care Unit
  • Deasy, J., Relaxing assumptions in deep probabilistic modelling
  • Bernstein, A., Immune Infiltrates in Breast Cancer: Clinical Significance from Histopathology to Prognosis
  • Contact Details

    Room: 
    FC20
    Office phone: 
    (01223) 7-63604
    Email: 

    pl219at@cam.ac.uk