| Machine and deep learning meet genome-scale metabolic modeling G Zampieri, S Vijayakumar, E Yaneske, C Angione PLoS computational biology 15 (7), e1007084, 2019 | 242 | 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 | 71 | 2020 |
| 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 | 51 | 2017 |
| A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria S Vijayakumar, P Rahman, C Angione iScience 23 (12), 101818, 2020 | 33 | 2020 |
| 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 | 23 | 2018 |
| Social dynamics modeling of chrono-nutrition A Di Stefano, M Scatà, S Vijayakumar, C Angione, A La Corte, P Liò PLoS Computational Biology 15 (1), 1-25, 2019 | 19 | 2019 |
| Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002 S Vijayakumar, C Angione STAR Protocols 2 (4), 100837, 2021 | 8 | 2021 |
| Role of Cyanobacteria in Biodeterioration of Historical Monuments—A Review S Vijayakumar BMR Microbiol 1 (1), 1-13, 2014 | 5 | 2014 |
| Potential applications of cyanobacteria in industrial effluents-a review. J Bioremed Biodeg 3: 1–6 S Vijayakumar | 5 | 2012 |
| A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling S Vijayakumar, G Magazzù, P Moon, A Occhipinti, C Angione Computational Systems Biology in Medicine and Biotechnology 2399, 87-122, 2022 | 2 | 2022 |
| Multi-omic Data Integration Elucidates Synechococcus Adaptation Mechanisms to Fluctuations in Light Intensity and Salinity S Vijayakumar, C Angione Bioinformatics and Biomedical Engineering: 5th International Work-Conference …, 2017 | 2 | 2017 |
| Kinetic modeling identifies targets for engineering improved photosynthetic efficiency in potato (Solanum tuberosum cv. Solara) S Vijayakumar, Y Wang, G Lehretz, S Taylor, E Carmo‐Silva, S Long The Plant Journal 117 (2), 561-572, 2024 | 1 | 2024 |
| Combining metabolic modelling with machine learning accurately predicts yeast growth rate C Culley, S Vijayakumar, G Zampieri, C Angione 11th International Workshop on Bio-Design Automation, 2019 | 1 | 2019 |
| Poly-omic statistical methods describe cyanobacterial metabolic adaptation to fluctuating environments S Vijayakumar, C Angione IWBDA 2017: 9th International Workshop on Bio-Design Automation, 2017 | | 2017 |
Claudio AngioneProfessor of Artificial Intelligence, Teesside University在 tees.ac.uk 的电子邮件经过验证
