Machine and deep learning meet genome-scale metabolic modelling G Zampieri, S Vijayakumar, E Yaneske, C Angione PLoS Computational Biology 15 (7), e1007084, 2019 | 242 | 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 | 186* | 2017 |
Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine C Angione BioMed Research International 2019, 2019 | 89 | 2019 |
Using machine learning as a surrogate model for agent-based simulations C Angione, E Silverman, E Yaneske PLOS ONE 17 (2), e0263150, 2022 | 76* | 2022 |
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 | 71 | 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 | 69 | 2021 |
Predictive analytics of environmental adaptability in multi-omic network models C Angione, P Lió Scientific reports 5, 2015 | 65 | 2015 |
Robust design of microbial strains J Costanza, G Carapezza, C Angione, P Lió, G Nicosia Bioinformatics 28 (23), 3097-3104, 2012 | 65 | 2012 |
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 | 64 | 2021 |
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 | 41 | 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 | 34 | 2020 |
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* | |
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 | 31 | 2022 |
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 | 29* | 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 | 28 | 2018 |
The poly-omics of ageing through individual-based metabolic modelling E Yaneske, C Angione BMC Bioinformatics 19 (14), 415, 2018 | 27 | 2018 |
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism C Angione Bioinformatics 34 (3), 494–501, 2018 | 27 | 2018 |
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 |