Claudio Angione

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Pietro LiòProfessor, University of CambridgeVerified email at cam.ac.uk
Guido ZampieriUniversità degli Studi di PadovaVerified email at unipd.it
Giuseppe NicosiaUniversity of Catania, ItalyVerified email at unict.it
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
Thierry PedrazziniUniversity of Lausanne Medical SchoolVerified email at chuv.ch
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
Yvan DevauxHead of Cardiovascular Research Unit, Luxembourg Institute of HealthVerified email at lih.lu
costanza emanueliImperial College LondonVerified email at imperial.ac.uk
Richard J ShawSchool of Health and Well-being, GlasgowVerified email at glasgow.ac.uk
Darek GoreckiUniversity of PortsmouthVerified email at port.ac.uk
Alessandro Di StefanoSenior Lecturer in Computer Science, Teesside University, SCEDT, UKVerified email at tees.ac.uk
Ploy N. PratanwanichDepartment of Mathematics and Computer Science, Faculty of Science, Chulalongkorn UniversityVerified email at math.sc.chula.ac.th
Giovanni StracquadanioPersonal Chair in Engineering Biology, The University of EdinburghVerified email at ed.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

Mechanism-aware AI Deep learning Metabolic modelling Systems 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	277	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	193*	2017
Using machine learning as a surrogate model for agent-based simulations C Angione, E Silverman, E Yaneske PLOS ONE 17 (2), e0263150, 2022	102*	2022
Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine C Angione BioMed Research International 2019, 2019	97	2019
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	80	2020
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, 1-15, 2021	77	2021
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	77	2021
Predictive analytics of environmental adaptability in multi-omic network models C Angione, P Lió Scientific reports 5, 2015	71	2015
Robust design of microbial strains J Costanza, G Carapezza, C Angione, P Lió, G Nicosia Bioinformatics 28 (23), 3097-3104, 2012	67	2012
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	62	2016
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	54	2022
A hybrid flux balance analysis and machine learning pipeline elucidates metabolic adaptation in cyanobacteria S Vijayakumar, PKSM Rahman, C Angione Iscience 23 (12), 2020	39	2020
Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities G Zampieri, S Campanaro, C Angione, L Treu Cell Reports Methods 3 (1), 2023	38	2023
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	34	2022
Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism F Eyassu, C Angione Royal Society Open Science 4 (10), 170360, 2017	32	2017
Modelling pyruvate dehydrogenase under hypoxia F Eyassu, C Angione	32*
Loss of full-length dystrophin expression results in major cell-autonomous abnormalities in proliferating myoblasts MRF Gosselin, V Mournetas, M Borczyk, S Verma, A Occhipinti, J Róg, ... Elife 11, e75521, 2022	30	2022
Dissecting the transcriptome in cardiovascular disease EL Robinson, AH Baker, M Brittan, I McCracken, G Condorelli, ... Cardiovascular Research 118 (4), 1004-1019, 2022	30	2022
In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production A Occhipinti, F Eyassu, TJ Rahman, PKSM Rahman, C Angione PeerJ 6, e6046, 2018	30	2018
The poly-omics of ageing through individual-based metabolic modelling E Yaneske, C Angione BMC Bioinformatics 19 (14), 415, 2018	30	2018

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