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

Background Co-expression measures are often used to define networks among genes.
Mutual information (MI) is often used as a generalized correlation measure. It is not clear …

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 …

Background To construct gene co-expression networks, it is necessary to evaluate the
correlation between different gene expression profiles. However, commonly used correlation …

Background The number of genomics and proteomics experiments is growing rapidly,
producing an ever-increasing amount of data that are awaiting functional interpretation. A …

Gene co-expression has been widely used to hypothesize gene function through guilt-by
association. However, it is not clear to what degree co-expression is informative, whether it …

Abstract Information regarding gene coexpression is useful to predict gene function. Several
databases have been constructed for gene coexpression in model organisms based on a …

Several methods were developed to mine gene–gene relationships from expression data.
Examples include correlation and mutual information methods for coexpression analysis …

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 …

Large-scale “omics” data, such as microarrays, can be used to infer underlying cellular
regulatory networks in organisms, enabling us to better understand the molecular basis of …