Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.
Increasing numbers of methodologies are available to find functional genomic clusters in RNA expression data. We describe a technique that computes comprehensive pair-wise mutual information for all genes in such a data set. An association with a high mutual information means that one gene is non-randomly associated with another; we hypothesize this means the two are related biologically. By picking a threshold mutual information and using only associations at or above the threshold, we show how this technique was used on a public data set of 79 RNA expression measurements of 2,467 genes to construct 22 clusters, or Relevance Networks. The biological significance of each Relevance Network is explained.
Butte
A J
AJ
Children's Hospital Informatics Program, Boston, MA 02115, USA.
Kohane
I S
IS
D003198:Computer Simulation; D015870:Gene Expression; D016678:Genome; D016681:Genome, Fungal; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D005810:Multigene Family; D012313:RNA / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics
D012313:RNA