Computer Laboratory

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(Here photos on Cambridge exam "lifestyle")

Pietro Lio'

I am a Reader (Full Professor from 1 October 2018) in Computational Biology in the Computer Laboratory which is the department of Computer Science of the University of Cambridge and I am a member of the Artificial Intelligence group of the Computer Laboratory.
I have a MA from Cambridge, a PhD in Complex Systems and Non Linear Dynamics (School of Informatics, dept of Engineering of the University of Firenze, Italy) and a PhD in (Theoretical) Genetics (University of Pavia, Italy).
Other Affliations: I am member of - the Integrate Cancer Medicine Institute,  the committee of MPhil in Computational Biology (Stakeholder Group for the CCBI) , steering committee of Cambridge BIG dataVPH-UK (Virtual Physiologycal Human), Fellow of Clare Hall College. I am Listed in www.topitalianscientists.org/Top_italian_scientists_VIA-Academy.aspx
Dept Administrative duties: Reviewer in University complaint and review procedure. I have successfully completed the equality and diversity essentials.

My research interest focuses on using bioinformatics, computational biology models and machine learning to integrate various types of data (molecular and clinical, drugs, social and lifestyle) across different spatial and temporal scales of biological complexity. In the context of basic science, these approaches are effective in understanding the mechanisms and the dynamics of how biological elements build up properties such as sensing the environment, information carrying, being programmable and doing computation and communication. In the context of biomedical fields, by integrating different layers of evidences, predictive models will improve the accuracy of diagnosis of complex diseases in presence of other chronic and acute conditions, will identify effective markers for disease trajectory and suggest composition of treatments (drugs and lifestyle) before the manifestation of symptoms.
I am happy to receive enquiries for PhD applications. Office: FC20; tel: +44 (0)1223-763604; E_mail: Pietro.Lio at  cl.cam.ac.uk


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Recent papers (2017-2018), Full list of Publications

Machine Learning and Bioinformatics Methods for Computational medicine, Big Health Data Integration and comorbidities

Editing a Springer Book on Automated Reasoning in Systems Biology and Medicine with P. Zuliani (Newcastle)

Hui Xiao, Krzysztof Bartoszek and Pietro Lio'. Multi-omic analysis of signaling factors in inflammatory comorbidities (BMC Bioinformatics in press)

Ioana Bica, Petar Velickovi ́c, Hui Xiao and Pietro Lio' (2018) Multi-omics data integration using cross-modal neural networks ESANN 2018 proceedings, European Symposium on Artificial Neural Networks, Computational  Intelligence  and Machine Learning.  Bruges (Belgium), 25-27 April 2018

Eugenio Del Prete, Angelo Facchiano, Pietro Liò  Bioinformatics methodologies for coeliac disease and its comorbidities (submitted).

Marco Barsacchi, Helena Andres-Terre, Pietro Lió GEESE: Metabolically driven latent space learning for gene expression data. Bioarxiv.

Francesco Bardozzo, Pietro Lió and Roberto Tagliaferri (2018) A study on multi-omic oscillations in Escherichia coli metabolic networks. BMC Bioinformatics201819 (Suppl 7) :194.

I Saggese, E Bona, M Conway, F Favero, M Ladetto, P Liò, G Manzini (2018) Flavio Mignone STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow BMC bioinformatics 19 (7), 184

Mancini et al. CiliateGEM: an open-project for ciliates metabolism analysis with Tetrahymena thermophila as model. A tool for predictions of metabolic variations and experimental condition design (BMC Bioinformatics, in press).

Andrea Tangherloni, Simone Spolaor, Leonardo Rundo, Marco S. Nobile, Paolo Cazzaniga, Giancarlo Mauri, Pietro Liò, Ivan Merelli, Daniela Besozzi GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly BMC Bioinformatics

Zakaria Benmounah et al., Parallel Swarm Intelligence Strategies for Large-scale Clustering based on MapReduce with Application to Epigenetics of Aging accepted in Applied Soft Computing

Di Stefano et al, Social Dynamics Modeling of Chrono-nutrition. Plos Comput. Biology (submitted)

Edgar Liberis Petar Velickovic, Pietro Sormanni, Michele Vendruscolo and Pietro Liò (2018) Parapred: Antibody Paratope Prediction using Convolutional and Recurrent Neural Networks. Bioinformatics 2018 Apr 16. doi: 10.1093/bioinformatics/bty305.

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio Graph Attention Networks. accepted at ICLR 2018

Scatà M, Di Stefano A, La Corte A, Liò P. (2018) Quantifying the propagation of distress and mental disorders in social networks. Sci Rep. 2018 Mar 22;8(1):5005. doi: 10.1038/s41598-018-23260-2.

Duo Wang, Mateja Jamnik, Pietro Liò (2018)  Investigating diagrammatic reasoning with deep
neural networks. Accepted at Diagrams 2018

Peng He, Tadashi Nakano, Yuming Mao, Pietro Liò, Qiang Liu, Kun Yang (2018) Stochastic Channel Switching of Frequency-Encoded Signals in Molecular Communication Networks. IEEE Communications Letters 22(2): 332-335 (2018)

Ezio Bartocci, Pietro Liò, Nicola Paoletti Guest Editors' Introduction to the Special Section on the 14th International Conference on Computational Methods in Systems Biology (CMSB 2016). IEEE/ACM Trans. Comput. Biology Bioinform. 15(4): 1122-1123 (2018)

Akhil Mathur, Tianlin Zhang, Sourav Bhattacharya, Petar Velickovic, Leonid Joffe, Nicholas D. Lane, Fahim Kawsar, Pietro Liò (2018) Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices. IPSN 2018: 200-211

Petar Veličković, Laurynas Karazija, Nicholas D. Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Liò, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville (2017) Cross-modal Recurrent Models for Human Weight Objective Prediction from Multimodal Time-series Data. NIPS ML4H & NIPS TSW

Momchil Peychev, Petar Velickovic, Pietro Liò (2017) Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders. 2017 NIPS Workshop on Learning Disentangled Representations

Emmanouil I Athanasiadis, Helena Andres, Jan G Botthof, Lauren Ferreira, Pietro Lio, Ana Cvejic (2017). Single-cell RNA-Sequencing uncovers transcriptional states and fate decisions in haematopoiesis. Nature Communications

Laurynas Karazija, Petar Velickovic, Pietro Liò (2018)  Automatic Inference of Cross-Modal Connection Topologies for X-CNNs. ISNN 2018: 54-63

Vijayakumar S., Conway M., Liò P. and Angione C. (2017) Multi-omic genome-scale models: methodologies, hands-on and perspectives. Springer Verlag

F. Tordini et al. (2017) Scientific Workflows on Clouds with Heterogeneous and Preemtible Instances. Proceedings ParCO

Bianchi L, Liò P. Opportunities for community awareness platforms in personal genomics and bioinformatics education. Brief Bioinform. 2017 Nov 1;18(6):1082-1090. doi: 10.1093/bib/bbw078

Martins DP, Barros MT, Pierobon M, Kandhavelu M, Lio P, Balasubramaniam S. (2017) Computational Models for Trapping Ebola Virus Using Engineered Bacteria. IEEE/ACM Transactions on Computational Biology and Bioinformatics

Brouwer T. and Liò P. (2017) Bayesian Hybrid Matrix Factorisation for Data Integration. Accepted at AISTATS 2017; PMLR 54:557-566

Vijayakumar S. Conway M., Liò P. and Angione C. (2017) Seeing the wood for the trees: a forest of methods for omic-network integration in metabolic modelling. Briefings in Bioinformatics

Brouwer T. and Liò P. (2017) Comparative Study of Inference Methods for Bayesian Matrix Factorisation. Accepted at ECML-PKDD 2017 (LNCS)

F. Tordini, I. Merelli, P. Liò, L. Milanesi, M. Aldinucci:  NuchaRt: Embedding High-Level Parallel Computing in R for Augmented Hi-C Data Analysis. Computational Intelligence Methods for Bioinformatics and Biostatistics Volume 9874 of the series Lecture Notes in Computer Science pp 259-272

M Barandalla, H Shi, H Xiao, S Colleoni, C Galli, P Lio, M. Trotter and G Lazzari (2017) Global gene expression profiling and senescence biomarker analysis of hESC exposed to H2O2 induced non-cytotoxic oxidative stress Stem Cell Research & Therapy 20178:160, DOI: 10.1186/s13287-017-0602-6

Moni M and Liò P. (2017) Genetic Profiling and Comorbidities of Zika Infection. Journal of Infectuous diseases (in Press).

Oshota et al. (2017) Transcriptome and Proteome Analysis of Salmonella enterica Serovar Typhimurium Systemic Infection of Wild Type and Immune-deficient Mice. Accepted in Plos One.

Heffernan K. , Liò P. and Teufel S. (2017) Multilayer Data and Document Stratification for Comorbidity Analysis (2017) Thirteenth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. LNBI

Dimitri MG and and Liò P. (2017) DrugClust: a machine learning approach for drugs side effects prediction. Computational Biology and Chemistry (10.1016/j.compbiolchem.2017.03.008).

S Saheb Kashaf,  C. Angione, P. Liò (2017) Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization. BMC Systems Biology.

Felicetti L, et al. Lio' P. (2017) A big-data layered architecture for analyzing molecular communications systems in blood vessels. Accepted at ACM NanoCom 2017

Veličković P., Wang, D., Lane N., Lio', P.  (2017)  X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets. IEEE SSCI 2016 (http://ssci2016.cs.surrey.ac.uk/)

Scatà M., Di Stefano A., La Corte A., Liò P. (2016) The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks, Scientific Reports 6, Article number: 37105

Machine Learning Methods in Image Analysis and Neuroinformatics

Spasov Simeon et al., A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer’s Disease, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18)

Duo Wang, Rui Zhang, Jin Zhu, Zhongzhao Teng, Yuan Huang, Filippo Spiga, Michael Hong-Fei Du, Jonathan H. Gillard, Qingsheng Lu, Pietro Liò (2018) Neural network fusion: a novel CT-MR aortic aneurysm image segmentation method. Medical Imaging: Image Processing 2018: 1057424

Jin Zhu, Duo Wang Pietro Liò (2017) A Multi-pathway 3D Dilated Convolutional Neural Network for Brain Tumor Segmentation. BRATS challenge

G M Dimitri S Agrawal Adam Young J Donnelly P Smielewski, P. Hutchinsons, M. Czosnyka, P Lio' C. Haubrich (2017) A multiplex network approach for the analysis of Intracranial Pressure and Heart Rate data in traumatic brain injured patients. Applied Network Science (in press)

Dimitri MG. et al., Computational challenges for the analysis of Intracranial Pressure and Heart Rate data in traumatic brain injuries patients. in press in Frontiers, (neuroinformatic workshop in Reading).

Awards

2018 - BITS Bioinformatics Italian Society- Torino
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2016: The paper “Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea” (M. Taffi first author) has won the 2016 BYRA first prize at ISEM (The International Society for Ecological Modelling Global Conference) 2016 and the MCED (Modelling Complex Ecological Dynamics) 2016 Award second prize.
 
2013 - Lagrange Fellowship (ISI, Universita' Piemonte Orientale)

2012 - Best Paper (Computing with MetabolicMachines) at Turing 100 in Manchester (first author Claudio Angione)

2011 - 3rd prize awarded by the European Commission (sponsored by ERCIM) for the  "Methodological bridges for complex systems" (with E. Merelli and N. Paoletti) at the FET 11, 2011  - Future and Emerging Technologies Conference ('Science Beyond Fiction')' conference  - Budapest

Public Engagement

I am active in science communication, and engage often with the media and in public science events (science cafe', European Researchers' Night, Coding Academy, etc). This is very important as a scientist is fundamentally a visionary who needs to interact with society.


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