Identifying conserved discriminative motifs

Jyotsna Kasturi, Raj Acharya, Ross Hardison

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

Abstract

The identification of regulatory motifs underlying gene expression is a challenging problem, particularly in eukaryotes. An algorithm to identify statistically significant discriminative motifs that distinguish between gene expression clusters is presented. The predictive power of the identified motifs is assessed with a supervised Naïve Bayes classifier. An information-theoretic feature selection criterion helps find the most informative motifs. Results on benchmark and real data demonstrate that our algorithm accurately identifies discriminative motifs. We show that the integration of comparative genomics information into the motif finding process significantly improves the discovery of discriminative motifs and overall classification accuracy.

Original languageEnglish (US)
Title of host publication3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
Pages334-348
Number of pages15
DOIs
StatePublished - Dec 5 2008
Event3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 - Melbourne, VIC, Australia
Duration: Oct 15 2008Oct 17 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5265 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
CountryAustralia
CityMelbourne, VIC
Period10/15/0810/17/08

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kasturi, J., Acharya, R., & Hardison, R. (2008). Identifying conserved discriminative motifs. In 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 (pp. 334-348). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5265 LNBI). https://doi.org/10.1007/978-3-540-88436-1-29