Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming-Reference-Cited by-同舟云学术

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

Published:2016-04-19 Issue:4 Volume:12 Page:e1004838
ISSN:1553-7358
Container-title:PLOS Computational Biology
language:en
Short-container-title:PLoS Comput Biol

Author:

Wu Stephen Gang,Wang Yuxuan,Jiang Wu,Oyetunde Tolutola,Yao Ruilian,Zhang Xuehong,Shimizu Kazuyuki,Tang Yinjie J.,Bao Forrest Sheng

Funder

National Science Foundation

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

Reference72 articles.

1. Fluxomics–connecting’omics analysis and phenotypes;G Winter;Environmental Microbiology,2013

2. Synergy between ¹³C-metabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in E. coli;X Chen;Metabolic Engineering,2011

3. Flux analysis of central metabolic pathways in Geobacter metallireducens during reduction of soluble Fe(III)-nitrilotriacetic acid;YJ Tang;Applied and Environmental Microbiology,2007

4. Recent advances in mapping environmental microbial metabolisms through ¹³C isotopic fingerprints;JKH Tang;Journal of The Royal Society Interface,2012

5. Metabolic engineering of Escherichia coli for direct production of 1, 4-butanediol;H Yim;Nature Chemical Biology,2011

Cited by 61 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning applications in precision medicine: Overcoming challenges and unlocking potential;TrAC Trends in Analytical Chemistry;2024-10

2. Machine learning for the advancement of genome-scale metabolic modeling;Biotechnology Advances;2024-09

3. Rethinking 13C-metabolic flux analysis – The Bayesian way of flux inference;Metabolic Engineering;2024-05

4. Current advances of the valorization technologies for heavy metal containing hyperaccumulators;Industrial Crops and Products;2024-03

5. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges;ACS Omega;2024-02-19