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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.

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 (January):

Teaching Geometric deep learning

Assessor 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

Journal articles

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Goh, CWJ., Bodnar, C. and Liò, P., 2022. Simplicial Attention Networks
  • Azevedo, T., Bethlehem, RAI., Whiteside, D., Swaddiwudhipong, N., Rowe, J., Lió, P. and Rittman, T., 2022. Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank
    Doi: 10.1101/2022.01.05.22268795
  • Borgheresi, R., Barucci, A., Colantonio, S., Aghakhanyan, G., Assante, M., Bertelli, E., Carlini, E., Carpi, R., Caudai, C., Cavallero, D., Cioni, D., Cirillo, R., Colcelli, V., Dell'Amico, A., Di Gangi, D., Erba, PA., Faggioni, L., Falaschi, Z., Gabelloni, M., Gini, R., Lelii, L., Liò, P., Lorito, A., Lucarini, S., Manghi, P., Mangiacrapa, F., Marzi, C., Mazzei, MA., Mercatelli, L., Mirabile, A., Mungai, F., Miele, V., Olmastroni, M., Pagano, P., Paiar, F., Panichi, G., Pascali, MA., Pasquinelli, F., Shortrede, JE., Tumminello, L., Volterrani, L., Neri, E. and NAVIGATOR Consortium Group, , 2022. NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients. Eur Radiol Exp, v. 6
    Doi: http://doi.org/10.1186/s41747-022-00306-9
  • Buterez, D., Bica, I., Tariq, I., Andrés-Terré, H. and Liò, P., 2022. CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks. Bioinformatics, v. 38
    Doi: 10.1093/bioinformatics/btab804
  • Meoni, G., Tenori, L., Schade, S., Licari, C., Pirazzini, C., Bacalini, MG., Garagnani, P., Turano, P., PROPAG-AGEING Consortium, , Trenkwalder, C., Franceschi, C., Mollenhauer, B. and Luchinat, C., 2022. Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients. NPJ Parkinsons Dis, v. 8
    Doi: http://doi.org/10.1038/s41531-021-00274-8
  • Barbero, F., Bodnar, C., Borde, HSDO., Bronstein, M., Veličković, P. and Liò, P., 2022. Sheaf Neural Networks with Connection Laplacians
  • Charoenkwan, P., Schaduangrat, N., Lio', P., Moni, MA., Shoombuatong, W. and Manavalan, B., 2022. Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. iScience, v. 25
    Doi: http://doi.org/10.1016/j.isci.2022.104883
  • Shadbahr, T., Roberts, M., Stanczuk, J., Gilbey, J., Teare, P., Dittmer, S., Thorpe, M., Torne, RV., Sala, E., Lio, P., Patel, M., Collaboration, AIX-COVNET., Rudd, JHF., Mirtti, T., Rannikko, A., Aston, JAD., Tang, J. and Schönlieb, C-B., 2022. Classification of datasets with imputed missing values: does imputation quality matter?
  • Charoenkwan, P., Schaduangrat, N., Hasan, MM., Moni, MA., Lió, P. and Shoombuatong, W., 2022. Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins. EXCLI J, v. 21
    Doi: 10.17179/excli2022-4723
  • Huang, J., Fang, Y., Nan, Y., Wu, H., Wu, Y., Gao, Z., Li, Y., Wang, Z., Lio, P., Rueckert, D., Eldar, YC. and Yang, G., 2022. Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
  • Lu, M., Christensen, C., Weber, J., Konno, T., Läubli, N., Scherer, K., Avezov, E., Lio, P., Lapkin, A., Kaminski Schierle, G. and Kaminski, C., 2022. ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology for video-rate super-resolution imaging
    Doi: 10.1101/2022.05.17.492189
  • Lu, X., Liao, Y., Liu, C., Lio, P. and Hui, P., 2022. Heterogeneous Model Fusion Federated Learning Mechanism Based on Model Mapping IEEE Internet of Things Journal, v. 9
    Doi: 10.1109/JIOT.2021.3110908
  • Chen, Y., Schonlieb, CB., Lio, P., Leiner, T., Dragotti, PL., Wang, G., Rueckert, D., Firmin, D. and Yang, G., 2022. AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis Proceedings of the IEEE, v. 110
    Doi: 10.1109/JPROC.2022.3141367
  • Liu, L., Huang, Z., Liò, P., Schönlieb, C-B. and Aviles-Rivero, AI., 2022. PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation
  • Yi, K., Chen, J., Wang, YG., Zhou, B., Liò, P., Fan, Y. and Hamann, J., 2022. Approximate Equivariance SO(3) Needlet Convolution
  • Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Lio, P., 2022. Encoding Concepts in Graph Neural Networks
  • 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
  • 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., Schaduangrat, N., Lio', P., Moni, MA., Manavalan, B. and Shoombuatong, W., 2022. NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides. Comput Biol Med, v. 148
    Doi: 10.1016/j.compbiomed.2022.105700
  • Dimitri, GM., Spasov, S., Duggento, A., Passamonti, L., Lió, P. and Toschi, N., 2022. Multimodal and multicontrast image fusion via deep generative models Information Fusion, v. 88
    Doi: http://doi.org/10.1016/j.inffus.2022.07.017
  • 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
  • Wang, Y., Wang, YG., Hu, C., Li, M., Fan, Y., Otter, N., Sam, I., Gou, H., Hu, Y., Kwok, T., Zalcberg, J., Boussioutas, A., Daly, RJ., Montúfar, G., Liò, P., Xu, D., Webb, GI. and Song, J., 2022. Cell graph neural networks enable the precise prediction of patient survival in gastric cancer. NPJ Precis Oncol, v. 6
    Doi: http://doi.org/10.1038/s41698-022-00285-5
  • 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
  • Dhillon, SK., Ganggayah, MD., Sinnadurai, S., Lio, P. and Taib, NA., 2022. Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis. Diagnostics (Basel), v. 12
    Doi: http://doi.org/10.3390/diagnostics12102526
  • Chaturvedi, A., Tiwari, A., Chaturvedi, S. and Lio, P., 2022. System Neural Network: Evolution and Change Based Structure Learning IEEE Transactions on Artificial Intelligence, v. 3
    Doi: http://doi.org/10.1109/TAI.2022.3143778
  • 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
  • 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
  • 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
  • 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
  • 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,
    Doi: http://doi.org/10.1016/j.jaad.2022.07.031
  • 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
  • Patel, S., Patel, S., Shah, RM., Doshi, S., Shah, S. and Lio, PA., 2022. Effects of sun protection on serum vitamin D deficiency. Photodermatol Photoimmunol Photomed,
    Doi: http://doi.org/10.1111/phpp.12838
  • 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
  • Yang, AF., Chun, KS., Yu, L., Walter, JR., Kim, D., Lee, JY., Jeong, H., Keller, MC., Seshadri, DR., Olagbenro, MO., Bae, JW., Reuther, W., Wu, E., Okamoto, K., Ikoma, A., Lio, PA., Fishbein, AB., Paller, AS. and Xu, S., 2022. Validation of a hand-mounted wearable sensor for scratching movements in adults with atopic dermatitis. J Am Acad Dermatol,
    Doi: http://doi.org/10.1016/j.jaad.2022.09.032
  • 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
  • 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
  • Kwatra, SG., De Bruin-Weller, M., Lio, P., Deleuran, M., Ofori, S., Teixeira, HD., Calimlim, B., Liu, Y. and Weidinger, S., 2022. 33152 Targeted combined endpoint improvement in patient and disease domains in atopic dermatitis (AD) among adults with moderate-to-severe AD treated with upadacitinib Journal of the American Academy of Dermatology, v. 87
    Doi: 10.1016/j.jaad.2022.06.864
  • 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
  • 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
  • Viñas, R., Joshi, CK., Georgiev, D., Dumitrascu, B., Gamazon, ER. and Liò, P., 2022. Hypergraph factorisation for multi-tissue gene expression imputation
    Doi: 10.1101/2022.07.31.502211
  • Lio, P., Anjuwon, S., Grivet-Seyve, M. and Emesiani, C., 2022. 33954 Efficacy, tolerability, and acceptability of a balm formulated with dimethicone 1% in type II diabetes patients with dry, cracked skin Journal of the American Academy of Dermatology, v. 87
    Doi: 10.1016/j.jaad.2022.06.684
  • 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
  • Buffelli, D., Liò, P. and Vandin, F., 2022. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
  • Lio, P., Simpson, E., Pierce, E., Cronin, A., McLean, R., Dave, SS., Kovacik, AJ., Feely, M. and Silverberg, J., 2022. 33273 Impact of atopic dermatitis lesion locations on patient burden: A real-world study Journal of the American Academy of Dermatology, v. 87
    Doi: 10.1016/j.jaad.2022.06.727
  • Charoenkwan, P., Schaduangrat, N., Lio, P., Moni, MA., Chumnanpuen, P. and Shoombuatong, W., 2022. iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides. ACS Omega, v. 7
    Doi: 10.1021/acsomega.2c04465
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  • Hagelberg, E., Goldman, N., Lió, P., Whelan, S., Schiefenhövel, W., Clegg, JB. and Bowden, DK., 1999. Evidence for mitochondrial DNA recombination in a human population of island Melanesia. Proc Biol Sci, v. 266
    Doi: http://doi.org/10.1098/rspb.1999.0663
  • Liò, P. and Goldman, N., 1999. Using protein structural information in evolutionary inference: transmembrane proteins. Mol Biol Evol, v. 16
    Doi: http://doi.org/10.1093/oxfordjournals.molbev.a026083
  • Mori, E., Liò, P., Daly, S., Damiani, G., Perito, B. and Fani, R., 1999. Molecular nature of RAPD markers from Haemophilus influenzae Rd genome. Res Microbiol, v. 150
    Doi: http://doi.org/10.1016/s0923-2508(99)80026-6
  • Liò, P. and Goldman, N., 1998. Review: Models of molecular evolution and phylogeny Genome Research, v. 8
    Doi: http://doi.org/10.1101/gr.8.12.1233
  • Liò, P. and Goldman, N., 1998. Models of molecular evolution and phylogeny. Genome Res, v. 8
    Doi: http://doi.org/10.1101/gr.8.12.1233
  • Lio, P. and Ruffo, S., 1998. Searching for genomic constraints NUOVO CIMENTO D, v. 20
  • Liò, P., Goldman, N., Thorne, JL. and Jones3, DT., 1998. PASSML: combining evolutionary inference and protein secondary structure prediction. Bioinformatics, v. 14
    Doi: http://doi.org/10.1093/bioinformatics/14.8.726
  • Liò, P., Goldman, N., Thorne, JL. and Jones, DT., 1998. PASSML: Combining evolutionary inference and protein secondary structure prediction Bioinformatics, v. 14
    Doi: 10.1093/bioinformatics/14.8.726
  • Dewar, J., Wheatley, A., Wilkinson, J., Holgate, ST., Thomas, NS., Lio, P., Morton, NE. and Hall, IP., 1997. Association of the Gln 27 beta 2-adrenoceptor polymorphism and IgE variability in asthmatic families. Chest, v. 111
    Doi: http://doi.org/10.1378/chest.111.6_supplement.78s
  • Fani, R., Tamburini, E., Mori, E., Lazcano, A., Liò, P., Barberio, C., Casalone, E., Cavalieri, D., Perito, B. and Polsinelli, M., 1997. Paralogous histidine biosynthetic genes: evolutionary analysis of the Saccharomyces cerevisiae HIS6 and HIS7 genes. Gene, v. 197
    Doi: http://doi.org/10.1016/s0378-1119(97)00146-7
  • Liò, P. and Morton, NE., 1997. Comparison of parametric and nonparametric methods to map oligogenes by linkage. Proc Natl Acad Sci U S A, v. 94
    Doi: http://doi.org/10.1073/pnas.94.10.5344
  • Bogani, P., Liò, P., Intrieri, MC. and Buiatti, M., 1997. A physiological and molecular analysis of the genus Nicotiana. Mol Phylogenet Evol, v. 7
    Doi: http://doi.org/10.1006/mpev.1996.0356
  • Dewar, JC., Wilkinson, J., Wheatley, A., Thomas, NS., Doull, I., Morton, N., Lio, P., Harvey, JF., Liggett, SB., Holgate, ST. and Hall, IP., 1997. The glutamine 27 beta2-adrenoceptor polymorphism is associated with elevated IgE levels in asthmatic families. J Allergy Clin Immunol, v. 100
    Doi: http://doi.org/10.1016/s0091-6749(97)70234-3
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  • Liò, P., Politi, A., Buiatti, M. and Ruffo, S., 1996. High statistics block entropy measures of DNA sequences. J Theor Biol, v. 180
    Doi: http://doi.org/10.1006/jtbi.1996.0091
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    Doi: http://doi.org/10.1139/g96-107
  • Bagnoli, F. and Liò, P., 1995. Selection, mutations and codon usage in a bacterial model. J Theor Biol, v. 173
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  • Fani, R., Liò, P. and Lazcano, A., 1995. Molecular evolution of the histidine biosynthetic pathway. J Mol Evol, v. 41
    Doi: http://doi.org/10.1007/BF00173156
  • VICARIO, F., VENDRAMIN, GG., ROSSI, P., LIO, P. and GIANNINI, R., 1995. ALLOZYME, CHLOROPLAST DNA AND RAPD MARKERS FOR DETERMINING GENETIC-RELATIONSHIPS BETWEEN ABIES-ALBA AND THE RELIC POPULATION OF ABIES NEBRODENSIS THEOR APPL GENET, v. 90
  • Lió, P., Ruffo, S. and Buiatti, M., 1994. Third codon G + C periodicity as a possible signal for an "internal" selective constraint. J Theor Biol, v. 171
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  • Reali, G. and Lio, P., Simulation Tools for Molecular Communications IEEE TCSIM Newsletter,
  • Jana, W., Lio, P. and Lapkin, A., Identification of strategic molecules for future circular supply chains using large reaction networks Reaction Chemistry and Engineering,
  • Athanasiadis, E., Botthof, J., Lio, P. and Cvejic, A., Single-cell RNA Sequencing uncovers transcriptional states and fate decisions in haematopoiesis Nature Communications,
  • Bonchak, JG. and Lio, PA., Nonpharmacologic interventions for chronic pruritus Itch, v. 5
    Doi: 10.1097/itx.0000000000000031
  • Beaini, D., Passaro, S., Letourneau, V., Hamilton, WL., Corso, G. and Lio, P., Directional Graph Networks INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, v. 139
  • Müller, T. and Lió, P., Personalisable Clinical Decision Support System. ERCIM News, v. 116
  • 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
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  • Tangherloni, A., Spolaor, S., Rundo, L., Nobile, MS., Cazzaniga, P., Mauri, G., Liò, P., Merelli, I. and Besozzi, D., GenHap: a novel computational method based on genetic algorithms for haplotype assembly. BMC Bioinformatics, v. 20
    Doi: http://doi.org/10.1186/s12859-019-2691-y
  • Ascolani, G. and Liò, P., Modeling breast cancer progression to bone: how driver mutation order and metabolism matter. BMC Medical Genomics, v. 12
    Doi: http://doi.org/10.1186/s12920-019-0541-4
  • Rocheteau, E., Liò, P. and Hyland, S., Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit ACM CHIL 2021 - Proceedings of the 2021 ACM Conference on Health, Inference, and Learning,
    Doi: 10.1145/3450439.3451860
  • Buterez, D., Janet, JP., Kiddle, S., Oglic, D. and Lio, P., Modelling local and general quantum mechanical properties with attention-based pooling Communications Chemistry,
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  • Deasy, J., Lio, P. and Ercole, A., Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing 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
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  • Bodnar, C., Cangea, C. and Lio, P., Deep Graph Mapper: Seeing Graphs through the Neural Lens Frontiers in Big Data,
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    Doi: http://doi.org/10.1609/aaai.v37i8.26090
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    Doi: http://doi.org/10.1016/j.neuroimage.2019.01.031
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  • 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
  • Deasy, J., Liò, P. and Ercole, A., Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation Scientific Reports,
    Doi: http://doi.org/10.1038/s41598-020-79142-z
  • Veličković, P., Wang, D., Lane, ND. and Liò, P., X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets SSCI 2016: 1-8,
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  • 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,
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  • 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,
  • 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
  • Dimitri, GM., Beqiri, E., Czosnyka, M., Ercole, A., Smielewski, P., Liò, P. and CENTER-TBI High Resolution Sub- Study Participants and Investigators, , Analysing cardio-cerebral crosstalks in an adult cohort from CENTER-TBI Acta Neurochirurgica: Supplementum,
    Doi: http://doi.org/10.1007/978-3-030-59436-7_9
  • Spergel, J., Blaiss, M., Lio, P., Kessel, A., Takiya, L., Werth, J., O'Connell, M., Zang, C. and Cork, M., Efficacy and Safety of Crisaborole in Patients With Mild-to-Moderate Atopic Dermatitis With and Without Comorbid Allergic Rhinitis SKIN The Journal of Cutaneous Medicine, v. 5
    Doi: 10.25251/skin.5.supp.13
  • Tailor, SA., Opolka, FL., Liò, P. and Lane, ND., Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions arxiv,
  • Dittmer, S., Roberts, M., Gilbey, J., biguri, A., preller, J., Rudd, J., Aston, J. and Schönlieb, C-B., Navigating the development challenges in creating complex data systems Nature Machine Intelligence,
  • Blaiss, M., Cork, M., Lio, P., Kessel, A., Takiya, L., Werth, J., O'Connell, M., Zang, C. and Spergel, J., Efficacy and Safety of Crisaborole in Patients With Mild-to-Moderate Atopic Dermatitis With and Without Food Allergies SKIN The Journal of Cutaneous Medicine, v. 5
    Doi: 10.25251/skin.5.supp.12
  • Laise, P., Fanelli, D., Lio, P. and Arcangeli, A., Modeling TGF-β signaling pathway in epithelial-mesenchymal transition AIP Advances, v. Special Topic: Physics of Cancer
  • Trębacz, M., Shams, Z., Jamnik, M., Scherer, P., Simidjievski, N., Terre, HA. and Liò, P., Using ontology embeddings for structural inductive bias in gene expression data analysis arxiv,
  • Banerjee, S., Lio, P., Jones, P. and Cardinal, R., A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness npj Schizophrenia,
    Doi: 10.1038/s41537-021-00191-y
  • Lu, M., Christensen, C., Weber, J., Konno, T., Laubli, N., Scherer, K., Avezov, E., Lio, P., Lapkin, A., Kaminski Schierle, G. and Kaminski, C., ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology Nature Methods,
    Doi: http://doi.org/10.1038/s41592-023-01815-0
  • Christensen, CN., Ward, E., Lio, P. and Kaminski, C., ML-SIM: Universal reconstruction of structured illumination microscopy images using transfer learning Biomedical Optics Express,
    Doi: http://doi.org/10.1364/boe.414680
  • King, J., Torné, RV., Campbell, A. and Liò, P., An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution
  • Scherer, P., Trębacz, M., Simidjievski, N., Viñas, R., Shams, Z., Terre, HA., Jamnik, M. and Liò, P., Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. Bioinformatics,
    Doi: 10.1093/bioinformatics/btab830
  • Callahan, SW. and Lio, PA., Current Therapies and Approaches to the Treatment of Chronic Itch International Journal of Clinical Reviews,
    Doi: 10.5275/ijcr.2012.02.01
  • Breger, A., Selby, I., Roberts, M., Babar, J., Gkrania-Klotsas, E., Preller, J., Escudero Sanchez, L., Rudd, J., Aston, J., Weir-McCall, J., Sala, E. and Schoenlieb, C., A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data Scientific Data,
  • Charoenkwan, P., Nantasenamat, C., Hasan, MM., Moni, MA., Lio', P. and Shoombuatong, W., iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features. International Journal of Molecular Sciences, v. 22
    Doi: 10.3390/ijms22168958
  • Deasy, J., Simidjievski, N. and Liò, P., Constraining Variational Inference with Geometric Jensen-Shannon Divergence Advances in Neural Information Processing Systems, v. 2020-December
  • Bodnar, C., Day, B. and Lió, P., Proximal Distilled Evolutionary Reinforcement Learning AAAI 2020 - 34th AAAI Conference on Artificial Intelligence,
  • Banerjee, S., Lio, P., Jones, PB. and Cardinal, RN., A Human-Interpretable Machine Learning Approach to Predict Mortality in Severe Mental Illness SSRN Electronic Journal,
    Doi: http://doi.org/10.2139/ssrn.3824766
  • Prokhorov, V., Pilehvar, MT., Kartsaklis, D., Lio, P. and Collier, N., Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces Proceedings of the AAAI Conference on Artificial Intelligence, v. 33
    Doi: 10.1609/aaai.v33i01.33016900
  • Dimitri, GM., Beqiri, E., Placek, MM., Czosnyka, M., Stocchetti, N., Ercole, A., Smielewski, P., Lio, P. and CENTER-TBI collaborators, , Modelling brain-heart cross-talks information in Traumatic Brain Injury patients Neurocritical Care,
    Doi: 10.1007/s12028-021-01353-7
  • Ganggayah, MD., Taib, NA., Har, YC., Lio, P. and Dhillon, SK., Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Medical Informatics and Decision Making, v. 19
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    Doi: 10.1007/978-3-030-73103-8_35
  • Lio, P., Physio-Environmental Sensing and Live Modeling interactive Journal of Medical Research (i-JMR), v. 2
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    Doi: 10.1093/bioinformatics/btab035
  • Conference proceedings

  • 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
  • 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
  • Bernárdez, G., Telyatnikov, L., Alarcón, E., Cabellos-Aparicio, A., Barlet-Ros, P. and Liò, P., 2023. Topological Network Traffic Compression GNNet 2023 - Proceedings of the 2nd Graph Neural Networking Workshop 2023,
    Doi: 10.1145/3630049.3630172
  • Mittone, G., Svoboda, F., Aldinucci, M., Lane, N. and Lió, P., 2023. A Federated Learning Benchmark for Drug-Target Interaction ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023,
    Doi: http://doi.org/10.1145/3543873.3587687
  • Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and van der Schaar, M., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis Proceedings of Machine Learning Research, v. 206
  • 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
  • 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
  • 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
  • Cardozo, S., Montero, GI., Kazhdan, D., Dimanov, B., Wijaya, M., Jamnik, M. and Lio, P., 2022. Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations CEUR Workshop Proceedings, v. 3318
  • Day, B., Viñas, R., Simidjievski, N. and Liò, P., 2022. Attentional Meta-learners for Few-shot Polythetic Classification Proceedings of Machine Learning Research, v. 162
  • Fan, J., Pei, J., Bi, X., Xiao, B. and Lio, P., 2022. Context Correlation Aware Network for Cardiac Segmentation Proceedings - IEEE International Conference on Multimedia and Expo, v. 2022-July
    Doi: http://doi.org/10.1109/ICME52920.2022.9859985
  • Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S. and Liò, P., 2022. 3D Infomax improves GNNs for Molecular Property Prediction Proceedings of Machine Learning Research, v. 162
  • Lu, X. and Lio, P., 2022. Second International Workshop On Artificial Intelligence To Security - AITS 2022 Proceedings - 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop Volume, DSN-W 2022,
    Doi: http://doi.org/10.1109/DSN-W54100.2022.00010
  • 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
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    Doi: 10.1109/PDP.2015.104
  • Lu, X., Lio, P., Hui, P. and Qu, Z., 2014. Nodes density adaptive opportunistic forwarding protocol for intermittently connected networks Proceedings - 2014 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2014,
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  • Felicetti, L., Femminella, M., Reali, G. and Liò, P., 2014. Endovascular mobile sensor network for detecting circulating tumoral cells BODYNETS 2014 - 9th International Conference on Body Area Networks,
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  • Bartoszek, K. and Lio, P., 2014. A novel algorithm to reconstruct phylogenies using gene sequences and expression data
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  • Liò, P., 2013. Methodologies for Systems Medicine: Time to Join the Forces of Bioengineering and Bioinformatics. BIOINFORMATICS,
  • 2013. Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013, Sicily, Italy, September 2-6, 2013 ECAL,
  • Lio, P., Iacovella, L., Bianchi, L. and Nguyen, V., 2013. Information Filtering and Learning: From Heuristics to Social Eudaimonia Proceedings of the European Conference on Complex Systems 2012,
  • Bansal, A., Azad, S. and Lio, P., 2013. Malaria Incidence Forecasting and Its Implication to Intervention Proceedings of the European Conference on Complex Systems 2012,
  • Angione, C., Carapezza, G., Costanza, J., Lio, P. and Nicosia, G., 2013. The Role of the Genome in the Evolution of the Complexity of Metabolic Machines Proceedings of the European Conference on Complex Systems 2012,
  • Bianchi, L., Fernandes, P. and Lio, P., 2013. Improving collective awareness and education about the privacy and ethical issues connected with the genome technologies The Future of Education, Conference Proceedings 2013,
  • Kim, H., Khoo, WM. and Lio, P., 2012. Polymorphic Attacks against Sequence-based Software Birthmarks
  • 2012. Artificial Immune Systems - 11th International Conference, ICARIS 2012, Taormina, Italy, August 28-31, 2012. Proceedings ICARIS, v. 7597
  • 2011. Artificial Immune Systems - 10th International Conference, ICARIS 2011, Cambridge, UK, July 18-21, 2011. Proceedings ICARIS, v. 6825
  • Lio, P., Emanuela Merelli, and Nicola Paoletti, NP., 2011. Multiple verification in computational modeling of bone pathologies EPTCS, v. 67
  • Merelli, E., Paoletti, N. and Lio, P., 2011. Methodological Bridges for Multi-Level Systems Procedia Computer Science, v. 7
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  • Bartoszek, K., Lio, P. and Sorathiya, A., 2010. INFLUENZA DIFFERENTIATION AND EVOLUTION SUMMER SOLSTICE 2009 INTERNATIONAL CONFERENCE ON DISCRETE MODELS OF COMPLEX SYSTEMS, v. 3
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  • Papini, A., Nicosia, G., Stracquadanio, G., Lio, P. and Umeton, R., 2010. Key Enzymes for the Optimization of CO2 Uptake and Nitrogen Consumption in the C-3 Photosynthetic Carbon Metabolism JOURNAL OF BIOTECHNOLOGY, v. 150
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  • Nguyen, VA. and Lio, P., 2009. Measuring similarity between gene expression profiles: a Bayesian approach BMC GENOMICS, v. 10
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  • Lu, Y-E., Roberts, SGB., Cheng, TMK., Dunbar, R., Liò, P. and Crowcroft, J., 2009. On optimising personal network size to manage information flow. CIKM-CNIKM,
  • Lu, Y-E., Roberts, SGB., Liò, P., Dunbar, R. and Crowcroft, J., 2009. Size Matters: Variation in Personal Network Size, Personality and Effect on Information Transmission. CSE (4),
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  • Xie, SK., Lio, P. and Lawniczak, AT., 2009. A Comparative Study of Noise Effect on Wavelet Based De-noising Methods IEEE TIC-STH 09: 2009 IEEE TORONTO INTERNATIONAL CONFERENCE: SCIENCE AND TECHNOLOGY FOR HUMANITY,
  • Xu, K., Hui, P., Li, VOK., Crowcroft, J., Latora, V. and Lio, P., 2009. Impact of Altruism on Opportunistic Communications 2009 FIRST INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS,
  • Kitchovitch, S., Song, YD., van der Wath, R. and Lio, P., 2009. Substitution Matrices and Mutual Information Approaches to Modeling Evolution LEARNING AND INTELLIGENT OPTIMIZATION, v. 5851
  • Kitchovitch, S., Leung, I., Song, YD. and Lio, P., 2009. Using Mutual Information and Models of Evolution for improved pattern detection 2009 INTERNATIONAL JOINT CONFERENCE ON BIOINFORMATICS, SYSTEMS BIOLOGY AND INTELLIGENT COMPUTING, PROCEEDINGS,
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  • Giampaolo Bella, GB. and Lio, P., 2009. Analysing the microRNA-17-92/Myc/E2F/RB Compound Toggle Switch by Theorem Proving Proc. of the 9th Workshop on Network Tools and Applications in Biology (Nettab’09), v. Liberodiscrivere (2009)
  • Sorathiya, A., Jucikas, T., Piecewicz, S., Sengupta, S. and Lio, P., 2009. Searching for Glycomics Role in Stem Cell Development COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, v. 5488
  • Guazzini, A., Lio, P., Passarella, A. and Conti, M., 2009. Information Processing and Timing Mechanisms in Vision ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT I, v. 5768
  • Bella, G. and Lio, P., 2009. Formal Analysis of the Genetic Toggle COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 5688
  • Sorathiyar, A., Lio, P. and Sguanci, L., 2009. Mathematical Model of HIV Superinfection and Comparative Drug Therapy ARTIFICIAL IMMUNE SYSTEMS, PROCEEDINGS, v. 5666
  • Xie, SK., Lio, P. and Lawniczak, AT., 2009. A Case Study of ICA with Multi-scale PCA of Simulated Traffic Data ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT II, v. 5769
  • Angelini, C., Cutillo, L., De Feis, I., Lio, P. and van der Wath, R., 2008. Combining experimental evidences from replicates and nearby species data for annotating novel genomes COLLECTIVE DYNAMICS: TOPICS ON COMPETITION AND COOPERATION IN THE BIOSCIENCES, v. 1028
  • Leung, IXY., Gibbs, G., Bagnoli, F., Sorathiya, A. and Lio, P., 2008. Contact Network Modeling of Flu Epidemics CELLULAR AUTOMATA, PROCEEDINGS, v. 5191
  • Lu, XF., Chen, YC., Leung, I., Xiong, Z. and Lio, P., 2008. A novel mobility model from a heterogeneous military MANET trace AD-HOC, MOBILE AND WIRELESS NETWORKS, PROCEEDINGS, v. 5198
  • van der Wath, RC., van der Wath, E., Carapelli, A., Nardi, F., Frati, F., Milanesi, L. and Lio, P., 2008. Bayesian phylogeny on grid BIOINFORMATICS RESEARCH AND DEVELOPMENT, PROCEEDINGS, v. 13
  • Xie, SK., Lawniczak, AT. and Lio, P., 2008. Parametric & non-parametric analysis of mean treatment effects of number of packets in transit in data network model 2008 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-4,
  • Lu, XF., Wicker, F., Lio', P. and Towsley, D., 2008. Security Estimation Model with Directional Antennas 2008 IEEE MILITARY COMMUNICATIONS CONFERENCE: MILCOM 2008, VOLS 1-7,
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  • Lu, YE., Lio, P. and Hand, S., 2008. Beta Random Projection BIO-INSPIRED COMPUTING AND COMMUNICATION, v. 5151
  • Schwarz, E., Leweke, FM., Bahn, S. and Lio, P., 2008. Combining molecular and physiological data of complex disorders BIOINFORMATICS RESEARCH AND DEVELOPMENT, PROCEEDINGS, v. 13
  • Allen, SM., Conti, M., Crowcroft, J., Dunbar, R., Lio, P., Mendes, JF., Molva, R., Passarella, A., Stavrakakis, I. and Whitaker, RM., 2008. Social Networking for Pervasive Adaptation SASOW 2008: SECOND IEEE INTERNATIONAL CONFERENCE ON SELF-ADAPTIVE AND SELF-ORGANIZING SYSTEMS WORKSHOPS, PROCEEDINGS,
  • Bagnoli, F., Guazzini, A. and Lio, P., 2008. Human Heuristics for Autonomous Agents BIO-INSPIRED COMPUTING AND COMMUNICATION, v. 5151
  • Lu, XF., Hui, P., Lio, P. and Xiong, Z., 2008. Identity Privacy Protection by Delayed Transmission in Pocket Switched Networks EUC 2008: PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING, VOL 2, WORKSHOPS,
  • Lio, P., Brilli, M. and Fani, R., 2008. Topological metrics in Blast data mining: Plasmid and nitrogen-fixing proteins case studies BIOINFORMATICS RESEARCH AND DEVELOPMENT, PROCEEDINGS, v. 13
  • Lio, P., Angelini, C., DeFeis, I., Nguyen, V., Cutillo, L. and va der Wath, R., 2008. Statistical issues for combining replicates and nearby species data and different omics Proceedings The Art and Science of Statistical Bioinformatics The 27th Leeds Annual Statistical Research Workshop 15th - 17th July 2008,
  • van der Wath, RC. and Lio, P., 2008. A Stochastic Single Cell Based Model of BrdU Measured Hematopoietic Stem Cell Kinetics COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, v. 5307
  • van der Wath, RC. and Lio, P., 2008. A Stochastic Multi-agent Model of Stem Cell Proliferation CELLULAR AUTOMATA, PROCEEDINGS, v. 5191
  • 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
  • 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
  • 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
  • Kershenbaum, A., Pappas, V., Lee, KW., Lio, P., Sadler, B. and Verma, D., 2008. A biologically-inspired MANET architecture - art. no. 698106 DEFENSE TRANSFORMATION AND NET-CENTRIC SYSTEMS 2008, v. 6981
  • Angelini, C., Cutillo, L., De Feis, I., Van der Wath, R. and Lio, P., 2007. Identifying regulatory sites using neighborhood species Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedings, v. 4447
  • Lawniczak, AT., Lio, P., Xie, S. and Xu, JY., 2007. Wavelet spectral analysis of packet traffic near phase transition point from free flow to congestion in data network model 2007 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3,
  • Fani, R., Brilli, M., Fondi, M. and Lio, P., 2007. The role of gene fusions in the evolution of metabolic pathways: the histidine biosynthesis case BMC EVOLUTIONARY BIOLOGY, v. 7
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  • 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
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  • 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,
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  • Sguanci, L., Bagnoli, F. and Lio, P., 2007. Modeling HIV quasispecies evolutionary dynamics BMC EVOLUTIONARY BIOLOGY, v. 7
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  • 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,
  • 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
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  • 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
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  • Dewar, J., Wheatley, A., Wilkinson, J., Holgate, ST., Thomas, NS., Lio, P., Morton, NE. and Hall, IP., 1997. Association of the Gln 27 beta(2)-adrenoceptor polymorphism and IgE variability in asthmatic families CHEST, v. 111
  • Morton, NE. and Lio, P., 1997. Oligogenic linkage and map integration GENETIC MAPPING OF DISEASE GENES,
  • 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),
  • Opolka, FL., Solomon, A., Cangea, C., Veličković, P., Liò, P. and Hjelm, RD., Spatio-Temporal Deep Graph Infomax
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  • 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,
  • Book chapters

  • Ciravegna, G., Giannini, F., Barbiero, P., Gori, M., Lio, P., Maggini, M. and Melacci, S., 2023. Learning logic explanations by neural networks
    Doi: http://doi.org/10.3233/FAIA230157
  • Ciravegna, G., Giannini, F., Barbiero, P., Gori, M., Lio, P., Maggini, M. and Melacci, S., 2023. Learning logic explanations by neural networks
    Doi: http://doi.org/10.3233/FAIA230157
  • De Maria, E., Despeyroux, J., Felty, A., Liò, P., Olarte, C. and Bahrami, A., 2023. Computational logic for biomedicine and neurosciences
    Doi: http://doi.org/10.1002/9781394229086.ch6
  • Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Liò, P., 2023. Concept Distillation in Graph Neural Networks
    Doi: 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
  • 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
  • Zafar, F. and Lio, P., 2021. Complementary and Alternative Medicine and Dermatooncology
    Doi: 10.1007/978-3-030-53437-0_12
  • 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
  • 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
  • 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
  • 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, 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: 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
  • 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
  • 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
  • Viñas, R., Andrés-Terré, H., Liò, P. and Bryson, K., 2019. Adversarial generation of gene expression data
    Doi: 10.1101/836254
  • 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

  • Deasy, J., Relaxing assumptions in deep probabilistic modelling
  • Bernstein, A., Immune Infiltrates in Breast Cancer: Clinical Significance from Histopathology to Prognosis
  • 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
  • Contact Details

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

    pl219at@cam.ac.uk