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

@inproceedings{citeulike:2239283, 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.}, address = {Children's Hospital Informatics Program, Boston, MA 02115, USA.}, author = {Butte, A. J. and Kohane, I. S. }, booktitle = {Pacific Symposium of Biocomputing}, citeulike-article-id = {2239283}, issn = {1793-5091}, journal = {Pac Symp Biocomput}, keywords = {genetic\_regulatory\_network, information\_theory, network\_inference}, pages = {418--429}, posted-at = {2008-01-16 13:45:04}, priority = {1}, title = {Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/10902190}, year = {2000} }

TY - CONF ID - citeulike:2239283 L3 - citeulike-article-id:2239283 TI - Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. T2 - Pacific Symposium of Biocomputing JF - Pac Symp Biocomput SP - 418 EP - 429 AD - Children's Hospital Informatics Program, Boston, MA 02115, USA. SN - 1793-5091 N2 - 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. KW - genetic_regulatory_network KW - information_theory KW - network_inference AU - Butte, AJ AU - Kohane, IS PY - 2000/// UR - http://view.ncbi.nlm.nih.gov/pubmed/10902190 ER -