Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements

Butte AJ, Kohane IS

Children's Hospital Informatics Program, Boston, MA 02115, USA.

Pac Symp Biocomput. 2000;:418-29.


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.

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