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STAR Protocols
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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 7002
Supreeta 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 growth
Christopher 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 modelling
Supreeta 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 Perspectives
Supreeta Vijayakumar, Max Conway, Pietro Lió, et al.
Plos Computational Biology
|
July 12, 2019
Machine and deep learning meet genome-scale metabolic modeling
Guido Zampieri, Supreeta Vijayakumar, Elisabeth Yaneske, et al.
Iscience
|
December 23, 2020
A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria
Supreeta 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 Modeling
Supreeta 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-nutrition
Alessandro Di Stefano, Marialisa Scatà, Supreeta Vijayakumar, et al.
Page
of 1
Search research articles
Search
Showing results (1-10 of 9) with videos related to
Sort By:
Page
of 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 7002
Supreeta 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 growth
Christopher 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 modelling
Supreeta 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 Perspectives
Supreeta Vijayakumar, Max Conway, Pietro Lió, et al.
Plos Computational Biology
|
July 12, 2019
Machine and deep learning meet genome-scale metabolic modeling
Guido Zampieri, Supreeta Vijayakumar, Elisabeth Yaneske, et al.
Iscience
|
December 23, 2020
A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria
Supreeta 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 Modeling
Supreeta 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-nutrition
Alessandro Di Stefano, Marialisa Scatà, Supreeta Vijayakumar, et al.
Page
of 1