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

ABSTRACT:

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 metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.

Results

We evaluated our framewor

SUBMITTER: Lee JY 

PROVIDER: S-EPMC8268592 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature