Exhaustive search of maximal biclusters in gene expression data

Yoshifumi Okada, Wataru Fujibuchi, Paul Horton

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

Abstract

Recently, several methods have been suggested to discover biclusters from gene expression data matrices, where a bicluster is defined as a subset of genes that exhibit a highly correlated expression pattern over a subset of conditions. Most of them produce sub-optimal biclusters with greedy or stochastic approach. In contrast, we propose a new biclustering method, BiModule, that exhaustively searches biclusters in a realistic time based on a closed itemset mining algorithm. Comparative experiments to salient biclustering methods are performed to test the validity of biclusters extracted by BiModule using synthetic data and real expression data. We show that BiModule provides high performance compared to the other methods in extracting artificially-embedded modules as well as modules strongly related to GO annotations and protein-protein interactions.

Original languageEnglish
Title of host publicationIMECS 2007 - International MultiConference of Engineers and Computer Scientists 2007
Pages307-312
Number of pages6
Publication statusPublished - 2007 Dec 1
EventInternational MultiConference of Engineers and Computer Scientists 2007, IMECS 2007 - Kowloon, Hong Kong
Duration: 2007 Mar 212007 Mar 23

Publication series

NameLecture Notes in Engineering and Computer Science
ISSN (Print)2078-0958

Other

OtherInternational MultiConference of Engineers and Computer Scientists 2007, IMECS 2007
CountryHong Kong
CityKowloon
Period07-03-2107-03-23

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)

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