A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.
flux balance analysis
machine learning
metabolic modeling
multimodal learning
systems biology
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
04 08 2020
04 08 2020
Historique:
pubmed:
18
7
2020
medline:
22
9
2020
entrez:
18
7
2020
Statut:
ppublish
Résumé
Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143
Identifiants
pubmed: 32675233
pii: 2002959117
doi: 10.1073/pnas.2002959117
pmc: PMC7414140
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
18869-18879Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/L013754/1
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors declare no competing interest.
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