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A Decomposition Approach for Discovering Discriminative Motifs in a Sequence Database*

Authors: 

David Lesaint, Deepak Mehta, Barry O'Sullivan, Vincent Vigneron

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
European Conference on Artificial Intelligence
Proceedings: 
21st European Conference on Artificial Intelligence
Digital Object Identifer (DOI): 
10.na
Publication Date: 
18/08/2014
Conference Location: 
Czech Republic
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No