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Claudio Angione
Claudio Angione
Professor of Artificial Intelligence, Teesside University
Verificeret mail på tees.ac.uk - Startside
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Citeret af
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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
2202019
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
177*2017
Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine
C Angione
BioMed Research International 2019, 2019
772019
Robust design of microbial strains
J Costanza, G Carapezza, C Angione, P Lió, G Nicosia
Bioinformatics 28 (23), 3097-3104, 2012
632012
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
622020
Predictive analytics of environmental adaptability in multi-omic network models
C Angione, P Lió
Scientific reports 5, 2015
602015
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
592016
Using machine learning as a surrogate model for agent-based simulations
C Angione, E Silverman, E Yaneske
PLOS ONE 17 (2), e0263150, 2022
54*2022
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
542021
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
462021
A hybrid flux balance analysis and machine learning pipeline elucidates metabolic adaptation in cyanobacteria
S Vijayakumar, PKSM Rahman, C Angione
Iscience 23 (12), 2020
312020
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
282022
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
272022
Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism
F Eyassu, C Angione
Royal Society Open Science 4 (10), 170360, 2017
272017
Modelling pyruvate dehydrogenase under hypoxia
F Eyassu, C Angione
27*
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism
C Angione
Bioinformatics 34 (3), 494–501, 2018
262018
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
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 Lió
BMC systems biology 11 (1), 1-13, 2017
262017
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
26*2016
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
242018
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Artikler 1–20