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Query-driven module discovery in microarray data

(2007) BIOINFORMATICS. 23(19). p.2573-2580
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Abstract
Motivation: Existing (bi) clustering methods for microarray data analysis often do not answer the specific questions of interest to a biologist. Such specific questions could be derived from other information sources, including expert prior knowledge. More specifically, given a set of seed genes which are believed to have a common function, we would like to recruit genes with similar expression profiles as the seed genes in a significant subset of experimental conditions. Results: We introduce QDB, a novel Bayesian query-driven biclustering framework in which the prior distributions allow introducing knowledge from a set of seed genes (query) to guide the pattern search. In two well-known yeast compendia, we grow highly functionally enriched biclusters from small sets of seed genes using a resolution sweep approach. In addition, relevant conditions are identified and modularity of the biclusters is demonstrated, including the discovery of overlapping modules. Finally, our method deals with missing values naturally, performs well on artificial data from a recent biclustering benchmark study and has a number of conceptual advantages when compared to existing approaches for focused module search.
Keywords
NETWORKS, GENE-EXPRESSION DATA, IDENTIFICATION, BIOLOGY

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Please use this url to cite or link to this publication:

Chicago
Dhollander, Thomas, Qizheng Sheng, Karen Lemmens, Bart De Moor, Kathleen Marchal, and Yves Moreau. 2007. “Query-driven Module Discovery in Microarray Data.” Bioinformatics 23 (19): 2573–2580.
APA
Dhollander, T., Sheng, Q., Lemmens, K., De Moor, B., Marchal, K., & Moreau, Y. (2007). Query-driven module discovery in microarray data. BIOINFORMATICS, 23(19), 2573–2580.
Vancouver
1.
Dhollander T, Sheng Q, Lemmens K, De Moor B, Marchal K, Moreau Y. Query-driven module discovery in microarray data. BIOINFORMATICS. 2007;23(19):2573–80.
MLA
Dhollander, Thomas, Qizheng Sheng, Karen Lemmens, et al. “Query-driven Module Discovery in Microarray Data.” BIOINFORMATICS 23.19 (2007): 2573–2580. Print.
@article{3186825,
  abstract     = {Motivation: Existing (bi) clustering methods for microarray data analysis often do not answer the specific questions of interest to a biologist. Such specific questions could be derived from other information sources, including expert prior knowledge. More specifically, given a set of seed genes which are believed to have a common function, we would like to recruit genes with similar expression profiles as the seed genes in a significant subset of experimental conditions. 
Results: We introduce QDB, a novel Bayesian query-driven biclustering framework in which the prior distributions allow introducing knowledge from a set of seed genes (query) to guide the pattern search. In two well-known yeast compendia, we grow highly functionally enriched biclusters from small sets of seed genes using a resolution sweep approach. In addition, relevant conditions are identified and modularity of the biclusters is demonstrated, including the discovery of overlapping modules. Finally, our method deals with missing values naturally, performs well on artificial data from a recent biclustering benchmark study and has a number of conceptual advantages when compared to existing approaches for focused module search.},
  author       = {Dhollander, Thomas and Sheng, Qizheng and Lemmens, Karen and De Moor, Bart and Marchal, Kathleen and Moreau, Yves},
  issn         = {1367-4803},
  journal      = {BIOINFORMATICS},
  keyword      = {NETWORKS,GENE-EXPRESSION DATA,IDENTIFICATION,BIOLOGY},
  language     = {eng},
  number       = {19},
  pages        = {2573--2580},
  title        = {Query-driven module discovery in microarray data},
  url          = {http://dx.doi.org/10.1093/bioinformatics/btm387},
  volume       = {23},
  year         = {2007},
}

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