A decomposition approach for discovering discriminative motifs in a sequence database

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

Abstract

This paper addresses the discovery of discriminative nary motifs in databases of labeled sequences. We consider databases made up of positive and negative sequences and define a motif as a set of patterns embedded in all positive sequences and subject to alignment constraints. We formulate constraints to eliminate redundant motifs and present a general constraint optimization framework to compute motifs that are exclusive to the positive sequences. We cast the discovery of closed and replication-free motifs in this framework and propose a two-stage approach whose last stage reduces to a minimum set covering problem. Experiments on protein sequence datasets demonstrate its efficiency.

Original languageEnglish
Title of host publicationECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
EditorsTorsten Schaub, Gerhard Friedrich, Barry O'Sullivan
PublisherIOS Press BV
Pages1057-1058
Number of pages2
ISBN (Electronic)9781614994183
DOIs
Publication statusPublished - 2014
Event21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic
Duration: 18 Aug 201422 Aug 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume263
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference21st European Conference on Artificial Intelligence, ECAI 2014
Country/TerritoryCzech Republic
CityPrague
Period18/08/1422/08/14

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