Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements (2000) [26 citations — 0 self]
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@INPROCEEDINGS{Butte00mutualinformation,author = {A. J. Butte and I. S. Kohane},
title = {Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements},
booktitle = {Pacific Symposium on Biocomputing},
year = {2000},
pages = {415--426}
}
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Abstract:
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. 1
