Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium
Bioinformatics
Computer sciences
Metabolism
Microbiology
Systems biology
Journal
STAR protocols
ISSN: 2666-1667
Titre abrégé: STAR Protoc
Pays: United States
ID NLM: 101769501
Informations de publication
Date de publication:
17 12 2021
17 12 2021
Historique:
entrez:
11
10
2021
pubmed:
12
10
2021
medline:
12
10
2021
Statut:
epublish
Résumé
Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to
Identifiants
pubmed: 34632416
doi: 10.1016/j.xpro.2021.100837
pii: S2666-1667(21)00543-8
pmc: PMC8488602
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
100837Informations de copyright
© 2021 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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