Discovering coherent value bicliques in genetic interaction data

Gowtham Atluri, Jeremy Bellay, Gaurav Pandey, Chad Myers, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Genetic Interaction (GI) data provides a means for exploring the structure and function of pathways in a cell. Coherent value bicliques (submatrices) in GI data represents functionally similar gene modules or protein complexes. However, no systematic approach has been proposed for exhaustively enumerating all coherent value submatrices in such data sets, which is the problem addressed in this paper. Using a monotonic range measure to capture the coherence of values in a submatrix of an input data matrix, we propose a two-step Apriori-based algorithm for discovering all nearly constant value submatrices, referred to as Range Constrained Blocks. By systematic evaluation on an extensive genetic interaction data set, we show that the coherent value submatrices represent groups of genes that are functionally related than the submatrices with diverse values. We also show that our approach can exhaustively find all the submatrices with a range less than a given threshold, while the other competing approaches can not find all such submatrices.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages125-132
Number of pages8
ISBN (Electronic)9781605583020
StatePublished - Jan 1 2010
Event9th International Workshop on Data Mining in Bioinformatics, BIOKDD 2010, Held in Conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining - Washington, United States
Duration: Jul 25 2010Jul 28 2010

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other9th International Workshop on Data Mining in Bioinformatics, BIOKDD 2010, Held in Conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
CountryUnited States
CityWashington
Period7/25/107/28/10

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