Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.

ABSTRACT: Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment

SUBMITTER: Jalili M 

PROVIDER: S-EPMC10199931 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature