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Supreeta Vijayakumar

Showing results (1-10 of 9) with videos related to

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STAR Protocols|October 11, 2021
Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium <i>Synechococcus</i> sp. PCC 7002Supreeta Vijayakumar, Claudio Angione
Proceedings of the National Academy of Sciences of the United States of America|July 18, 2020
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growthChristopher Culley, Supreeta Vijayakumar, Guido Zampieri, et al.
Briefings in Bioinformatics|June 3, 2017
Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modellingSupreeta Vijayakumar, Max Conway, Pietro Lió, et al.
Methods in Molecular Biology (Clifton, N.J.)|December 10, 2017
Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and PerspectivesSupreeta Vijayakumar, Max Conway, Pietro Lió, et al.
Plos Computational Biology|July 12, 2019
Machine and deep learning meet genome-scale metabolic modelingGuido Zampieri, Supreeta Vijayakumar, Elisabeth Yaneske, et al.
Iscience|December 23, 2020
A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in CyanobacteriaSupreeta Vijayakumar, Pattanathu K S M Rahman, Claudio Angione
Methods in Molecular Biology (Clifton, N.J.)|May 23, 2022
A Practical Guide to Integrating Multimodal Machine Learning and Metabolic ModelingSupreeta Vijayakumar, Giuseppe Magazzù, Pradip Moon, et al.
The Plant Journal : for Cell and Molecular Biology|November 3, 2023
Kinetic modeling identifies targets for engineering improved photosynthetic efficiency in potato (Solanum tuberosum cv. Solara)Supreeta Vijayakumar, Yu Wang, Günter Lehretz, et al.
Plos Computational Biology|January 31, 2019
Social dynamics modeling of chrono-nutritionAlessandro Di Stefano, Marialisa Scatà, Supreeta Vijayakumar, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
STAR Protocols|October 11, 2021
Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium <i>Synechococcus</i> sp. PCC 7002Supreeta Vijayakumar, Claudio Angione
Proceedings of the National Academy of Sciences of the United States of America|July 18, 2020
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growthChristopher Culley, Supreeta Vijayakumar, Guido Zampieri, et al.
Briefings in Bioinformatics|June 3, 2017
Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modellingSupreeta Vijayakumar, Max Conway, Pietro Lió, et al.
Methods in Molecular Biology (Clifton, N.J.)|December 10, 2017
Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and PerspectivesSupreeta Vijayakumar, Max Conway, Pietro Lió, et al.
Plos Computational Biology|July 12, 2019
Machine and deep learning meet genome-scale metabolic modelingGuido Zampieri, Supreeta Vijayakumar, Elisabeth Yaneske, et al.
Iscience|December 23, 2020
A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in CyanobacteriaSupreeta Vijayakumar, Pattanathu K S M Rahman, Claudio Angione
Methods in Molecular Biology (Clifton, N.J.)|May 23, 2022
A Practical Guide to Integrating Multimodal Machine Learning and Metabolic ModelingSupreeta Vijayakumar, Giuseppe Magazzù, Pradip Moon, et al.
The Plant Journal : for Cell and Molecular Biology|November 3, 2023
Kinetic modeling identifies targets for engineering improved photosynthetic efficiency in potato (Solanum tuberosum cv. Solara)Supreeta Vijayakumar, Yu Wang, Günter Lehretz, et al.
Plos Computational Biology|January 31, 2019
Social dynamics modeling of chrono-nutritionAlessandro Di Stefano, Marialisa Scatà, Supreeta Vijayakumar, et al.
Pageof 1