Co-expression networks have proven effective at assigning putative functions to genes
based on the functional annotation of their co-expressed partners, in candidate gene …

Gene co-expression analysis has emerged as a powerful method to provide insights into
gene function and regulation. The rapid growth of publicly available RNA-sequencing (RNA …

Motivation: RNA-seq co-expression analysis is in its infancy and reasonable practices
remain poorly defined. We assessed a variety of RNA-seq expression data to determine …

Background Since experimental elucidation of gene function is often laborious, various in
silico methods have been developed to predict gene function of uncharacterized genes …

Background Constructing gene coexpression networks is a powerful approach for analyzing
high-throughput gene expression data towards module identification, gene function …

Background Analysis of gene expression data using genome-wide microarrays is a
technique often used in genomic studies to find coexpression patterns and locate groups of …

Gene co-expression networks are a powerful type of analysis to construct gene groupings
based on transcriptomic profiling. Co-expression networks make it possible to discover …

Gene co-expression networks can be used to associate genes of unknown function with
biological processes, to prioritize candidate disease genes or to discern transcriptional …

RNA-Seq is quickly becoming the preferred method for comprehensively characterizing
whole transcriptome activity and the analysis of count data from RNA-Seq requires new …

Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA
variants. Variation in gene expression underlies many biological processes and holds a key …