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

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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Bibliographic Details
Main Authors: Lee, J.Y (Author), Nguyen, B. (Author), Orosco, C. (Author), Styczynski, M.P (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Allosteric regulation
allosterism
article
Biomolecules
diagnostic test accuracy study
Experimental methods
fluxomics
Fluxomics
gene expression regulation
Gene Expression Regulation
gene regulatory network
Gene Regulatory Networks
High sampling frequencies
machine learning
Machine learning
Machine Learning
Metabolic Networks and Pathways
Metabolic regulations
metabolism
Metabolism
Metabolites
metabolomics
Metabolomics
noise
Positive predictive values
Predictive analytics
predictive value
Regulatory interactions
Regulatory structure
Sampling frequencies
Scour
Topology
Transcriptional regulation
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