A Bayesian Framework for the Classification of Microbial Gene Activity States.

ABSTRACT: Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene activity for each gene in each experimental condition as belonging to one of two states: active (the gene product is part of an active cellular mechanism) or inactive (the cellular mechanism is not active). These existing methods of classifying gene activity states suffer from multiple limitations, including enforcing unrealistic constraints on the overall proportions of active and inactive genes, failing to leverage a priori knowledge of gene co-regulation, failing to account for differences betwe

SUBMITTER: Disselkoen C 

PROVIDER: S-EPMC4977825 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature