Claudio Angione

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P. Lió, Pietro Lio, P. Lio, Pietro Liò, Pie...University of CambridgeVerified email at cam.ac.uk
Giuseppe NicosiaUniversity of Catania, ItalyVerified email at unict.it
Guido ZampieriUniversità degli Studi di PadovaVerified email at unipd.it
Supreeta VijayakumarLancaster Environment Centre, Lancaster UniversityVerified email at lancaster.ac.uk
Jole CostanzaBioinformatician, Nerviano Medical SciencesVerified email at nervianoms.com
Annalisa OcchipintiTeesside UniversityVerified email at tees.ac.uk
Pattanathu K.S.M. RahmanAcademic member at LJMUVerified email at teegene.co.uk
Sandra PucciarelliUniversity of CamerinoVerified email at unicam.it
Alessandro Di StefanoSenior Lecturer in Computer Science, Teesside University, SCEDT, UKVerified email at tees.ac.uk
Ploy N. PratanwanichVerified email at math.sc.chula.ac.th
Richard J ShawSchool of Health and Well-being, GlasgowVerified email at glasgow.ac.uk
Giovanni StracquadanioSenior Lecturer in Synthetic Biology, The University of EdinburghVerified email at ed.ac.uk
Thierry PedrazziniUniversity of Lausanne Medical SchoolVerified email at chuv.ch
costanza emanueliImperial CollegeVerified email at imperial.ac.uk
Carlo GaetanoLaboratorio di Epigenetica-Istituti Clinici Scientifici Maugeri, Pavia, Italy.Verified email at icsmaugeri.it
Fabio MartelliIRCCS- Policlinico San Donato, Molecular Cardiology LaboratoryVerified email at grupposandonato.it
Prashant K SrivastavaImperial CollegeVerified email at imperial.ac.uk
Michelangelo CeciUniversity of Bari "A. Moro"Verified email at di.uniba.it
Gianvito PioAssistant Professor, University of BariVerified email at uniba.it
Nicola PaolettiDepartment of Informatics, King's College LondonVerified email at kcl.ac.uk

Claudio Angione

Professor of Artificial Intelligence, Teesside University

Verified email at tees.ac.uk - Homepage

Machine learning Metabolic modelling Computational biology Multi-omics integration


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Machine and deep learning meet genome-scale metabolic modelling G Zampieri, S Vijayakumar, E Yaneske, C Angione PLoS Computational Biology 15 (7), e1007084, 2019	203	2019
Seeing the wood for the trees: a forest of methods for optimisation and omic-network integration in metabolic modelling S Vijayakumar, M Conway, P Lió, C Angione Briefings in Bioinformatics, 2017	172*	2017
Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine C Angione BioMed Research International 2019, 2019	73	2019
Robust design of microbial strains J Costanza, G Carapezza, C Angione, P Lió, G Nicosia Bioinformatics 28 (23), 3097-3104, 2012	63	2012
Predictive analytics of environmental adaptability in multi-omic network models C Angione, P Lió Scientific reports 5, 2015	59	2015
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth C Culley, S Vijayakumar, G Zampieri, C Angione Proceedings of the National Academy of Sciences 117 (31), 18869-18879, 2020	57	2020
Multiplex methods provide effective integration of multi-omic data in genome-scale models C Angione, M Conway, P Lió BMC bioinformatics 17 (4), 257-269, 2016	57	2016
Integrated multi-omics analysis of ovarian cancer using variational autoencoders MT Hira, MA Razzaque, C Angione, J Scrivens, S Sawan, M Sarker Scientific reports 11 (1), 6265, 2021	50	2021
Using machine learning as a surrogate model for agent-based simulations C Angione, E Silverman, E Yaneske PLOS ONE 17 (2), e0263150, 2022	44*	2022
Situating agent-based modelling in population health research E Silverman, U Gostoli, S Picascia, J Almagor, M McCann, R Shaw, ... Emerging Themes in Epidemiology 18 (1), 1-15, 2021	44	2021
A hybrid flux balance analysis and machine learning pipeline elucidates metabolic adaptation in cyanobacteria S Vijayakumar, PKSM Rahman, C Angione Iscience 23 (12), 2020	30	2020
Making life difficult for Clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization SS Kashaf, C Angione, P Lió BMC systems biology 11 (1), 1-13, 2017	26	2017
Making life difficult for Clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization SS Kashaf, C Angione, P Lió BMC systems biology 11 (1), 1-13, 2017	26	2017
Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction G Pio, P Mignone, G Magazzù, G Zampieri, M Ceci, C Angione Bioinformatics 38 (2), 487-493, 2022	25	2022
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism C Angione Bioinformatics 34 (3), 494–501, 2018	25	2018
Bioinformatics Challenges and Potentialities in Studying Extreme Environments C Angione, P Liò, S Pucciarelli, B Can, M Conway, M Lotti, H Bokhari, ... International Meeting on Computational Intelligence Methods for …, 2016	25*	2016
Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism F Eyassu, C Angione Royal Society Open Science 4 (10), 170360, 2017	24	2017
Modelling pyruvate dehydrogenase under hypoxia F Eyassu, C Angione	24*
The poly-omics of ageing through individual-based metabolic modelling E Yaneske, C Angione BMC Bioinformatics 19 (14), 415, 2018	23	2018
A pipeline and comparative study of 12 machine learning models for text classification A Occhipinti, L Rogers, C Angione Expert Systems with Applications 201, 117193, 2022	21	2022

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