A latent variable model for chemogenomic profiling. Export

@article{citeulike:525481, abstract = {MOTIVATION: In haploinsufficiency profiling data, pleiotropic genes are often misclassified by clustering algorithms that impose the constraint that a gene or experiment belong to only one cluster. We have developed a general probabilistic model that clusters genes and experiments without requiring that a given gene or drug only appear in one cluster. The model also incorporates the functional annotation of known genes to guide the clustering procedure. RESULTS: We applied our model to the clustering of 79 chemogenomic experiments in yeast. Known pleiotropic genes PDR5 and MAL11 are more accurately represented by the model than by a clustering procedure that requires genes to belong to a single cluster. Drugs such as miconazole and fenpropimorph that have different targets but similar off-target genes are clustered more accurately by the model-based framework. We show that this model is useful for summarizing the relationship among treatments and genes affected by those treatments in a compendium of microarray profiles. AVAILABILITY: Supplementary information and computer code at http://genomics.lbl.gov/llda.}, address = {Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. flaherty@berkeley.edu}, author = {Flaherty, P. and Giaever, G. and Kumm, J. and Jordan, M. I. and Arkin, A. P.}, citeulike-article-id = {525481}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/15919724}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=15919724}, day = {1}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {thesis3}, month = {August}, number = {15}, pages = {3286--3293}, posted-at = {2006-03-01 16:40:04}, priority = {4}, title = {A latent variable model for chemogenomic profiling.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/15919724}, volume = {21}, year = {2005} }

TY - JOUR ID - citeulike:525481 L3 - citeulike-article-id:525481 N2 - MOTIVATION: In haploinsufficiency profiling data, pleiotropic genes are often misclassified by clustering algorithms that impose the constraint that a gene or experiment belong to only one cluster. We have developed a general probabilistic model that clusters genes and experiments without requiring that a given gene or drug only appear in one cluster. The model also incorporates the functional annotation of known genes to guide the clustering procedure. RESULTS: We applied our model to the clustering of 79 chemogenomic experiments in yeast. Known pleiotropic genes PDR5 and MAL11 are more accurately represented by the model than by a clustering procedure that requires genes to belong to a single cluster. Drugs such as miconazole and fenpropimorph that have different targets but similar off-target genes are clustered more accurately by the model-based framework. We show that this model is useful for summarizing the relationship among treatments and genes affected by those treatments in a compendium of microarray profiles. AVAILABILITY: Supplementary information and computer code at http://genomics.lbl.gov/llda. IS - 15 JF - Bioinformatics SN - 1367-4803 AD - Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. flaherty@berkeley.edu EP - 3293 TI - A latent variable model for chemogenomic profiling. VL - 21 SP - 3286 KW - thesis3 AU - Flaherty, P AU - Giaever, G AU - Kumm, J AU - Jordan, MI AU - Arkin, AP PY - 2005/08/01/ UR - http://view.ncbi.nlm.nih.gov/pubmed/15919724 ER -