Follow
Supreeta Vijayakumar
Supreeta Vijayakumar
Lancaster Environment Centre, Lancaster University
Verified email at lancaster.ac.uk - Homepage
Title
Cited by
Cited by
Year
Machine and deep learning meet genome-scale metabolic modeling
G Zampieri, S Vijayakumar, E Yaneske, C Angione
PLoS computational biology 15 (7), e1007084, 2019
1722019
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
482020
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
452017
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
242020
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
192018
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
152019
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
22021
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
22017
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
12022
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
12019
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
The system can't perform the operation now. Try again later.
Articles 1–11