| Machine and deep learning meet genome-scale metabolic modelling G Zampieri, S Vijayakumar, E Yaneske, C Angione PLoS Computational Biology 15 (7), e1007084, 2019 | 166 | 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 | 155* | 2017 |
| Robust design of microbial strains J Costanza, G Carapezza, C Angione, P Lió, G Nicosia Bioinformatics 28 (23), 3097-3104, 2012 | 64 | 2012 |
| Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine C Angione BioMed Research International 2019, 2019 | 62 | 2019 |
| Predictive analytics of environmental adaptability in multi-omic network models C Angione, P Lió Scientific reports 5, 2015 | 56 | 2015 |
| 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 | 54 | 2016 |
| 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 | 44 | 2020 |
| 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 | 32 | 2021 |
| 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 | 28 | 2021 |
| 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 | 25 | 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 | 25 | 2017 |
| Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism C Angione Bioinformatics 34 (3), 494–501, 2018 | 24 | 2018 |
| A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria S Vijayakumar, PKSM Rahman, C Angione Iscience 23 (12), 101818, 2020 | 22 | 2020 |
| The poly-omics of ageing through individual-based metabolic modelling E Yaneske, C Angione BMC Bioinformatics 19 (14), 415, 2018 | 21 | 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 | 21* | 2016 |
| Using machine learning as a surrogate model for agent-based simulations C Angione, E Silverman, E Yaneske PLOS ONE 17 (2), e0263150, 2022 | 20* | 2022 |
| Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives S Vijayakumar, M Conway, P Lió, C Angione Metabolic Network Reconstruction and Modeling: Methods and Protocols, 389-408, 2018 | 19 | 2018 |
| Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism F Eyassu, C Angione Royal Society Open Science 4 (10), 170360, 2017 | 19 | 2017 |
| A hybrid of metabolic flux analysis and bayesian factor modeling for multiomic temporal pathway activation C Angione, N Pratanwanich, P Lió ACS synthetic biology 4 (8), 880-889, 2015 | 19 | 2015 |
| Modelling pyruvate dehydrogenase under hypoxia F Eyassu, C Angione | 19* | |
P. Lió, Pietro Lio, P. Lio, Pietro Liò, Pie...University of CambridgeVerified email at cam.ac.uk
