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 …

Comparing the gene-expression profiles of sick and healthy individuals can help in
understanding disease. Such differential expression analysis is a well-established way to …

Analysis of gene expression data is widely used in transcriptomic studies to understand
functions of molecules inside a cell and interactions among molecules. Differential co …

Background It is thought that genes with similar patterns of mRNA expression and genes
with similar functions are likely to be regulated via the same mechanisms. It has been …

Background Elucidation of regulatory networks, including identification of regulatory
mechanisms specific to a given biological context, is a key aim in systems biology. This has …

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 …

Background Co-expression correlations provide the ability to predict gene functionality
within specific biological contexts, such as different tissue and disease conditions. However …

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 …

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 …

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