SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BACKGROUND: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model...

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Detalles Bibliográficos
Autores principales: Lee, Justin Y., Nguyen, Britney, Orosco, Carlos, Styczynski, Mark P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268592/
https://www.ncbi.nlm.nih.gov/pubmed/34238207
http://dx.doi.org/10.1186/s12859-021-04281-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268592/
https://www.ncbi.nlm.nih.gov/pubmed/34238207
http://dx.doi.org/10.1186/s12859-021-04281-7

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