A latent variable model for chemogenomic profiling. Flaherty, P., Giaever, G., Kumm, J., Jordan, M. I, & Arkin, A. P Bioinformatics, 21(15):3286--3293, Aug, 2005.
doi  abstract   bibtex   
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.
@article{Flaherty:2005cy,
	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.},
	Author = {Flaherty, Patrick and Giaever, Guri and Kumm, Jochen and Jordan, Michael I and Arkin, Adam P},
	Date-Added = {2015-03-09 17:51:14 +0000},
	Date-Modified = {2015-03-09 17:51:21 +0000},
	Doi = {10.1093/bioinformatics/bti515},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Computer Simulation; Gene Deletion; Gene Expression Profiling; Gene Expression Regulation, Fungal; Models, Genetic; Models, Statistical; Oligonucleotide Array Sequence Analysis; Pharmaceutical Preparations; Pharmacogenetics; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins},
	Month = {Aug},
	Number = {15},
	Pages = {3286--3293},
	Pmid = {15919724},
	Pst = {ppublish},
	Title = {A latent variable model for chemogenomic profiling},
	Volume = {21},
	Year = {2005},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/bti515}}

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