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Claudio Angione
Claudio Angione
Professor of Artificial Intelligence, Teesside University
Verified email at tees.ac.uk - Homepage
Title
Cited by
Cited by
Year
Machine and deep learning meet genome-scale metabolic modelling
G Zampieri, S Vijayakumar, E Yaneske, C Angione
PLoS Computational Biology 15 (7), e1007084, 2019
2032019
Seeing the wood for the trees: a forest of methods for optimisation and omic-network integration in metabolic modelling
S Vijayakumar, M Conway, P Liˇ, 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
732019
Robust design of microbial strains
J Costanza, G Carapezza, C Angione, P Liˇ, G Nicosia
Bioinformatics 28 (23), 3097-3104, 2012
632012
Predictive analytics of environmental adaptability in multi-omic network models
C Angione, P Liˇ
Scientific reports 5, 2015
592015
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
572020
Multiplex methods provide effective integration of multi-omic data in genome-scale models
C Angione, M Conway, P Liˇ
BMC bioinformatics 17 (4), 257-269, 2016
572016
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
502021
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
442021
A hybrid flux balance analysis and machine learning pipeline elucidates metabolic adaptation in cyanobacteria
S Vijayakumar, PKSM Rahman, C Angione
Iscience 23 (12), 2020
302020
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 Liˇ
BMC systems biology 11 (1), 1-13, 2017
262017
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 Liˇ
BMC systems biology 11 (1), 1-13, 2017
262017
Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction
G Pio, P Mignone, G Magazz¨, G Zampieri, M Ceci, C Angione
Bioinformatics 38 (2), 487-493, 2022
252022
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism
C Angione
Bioinformatics 34 (3), 494–501, 2018
252018
Bioinformatics Challenges and Potentialities in Studying Extreme Environments
C Angione, P Li˛, S Pucciarelli, B Can, M Conway, M Lotti, H Bokhari, ...
International Meeting on Computational Intelligence Methods forá…, 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
242017
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
232018
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
212022
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