Evaluating the Significance of Sequence Motifs by the Minimum Description Length Principle

Qicheng Ma, Jason T.L. Wang

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

Abstract

Sdiscover is a tool capable of finding subsequences, possibly separated by arbitrarily long gaps, in a set of sequences. These subsequences are referred to as motifs. This paper proposes a method to evaluate the significance of the sequence motifs found by Sdiscover. The method is based on the minimum description length principle and Shannon's coding theory. The equivalence of the proposed method to the Bayesian inference is also discussed.

Original languageEnglish (US)
Title of host publicationProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 2
EditorsP.P. Wang, P.P. Wang
Pages798-801
Number of pages4
Edition2
StatePublished - 2000
EventProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 - Atlantic City, NJ, United States
Duration: Feb 27 2000Mar 3 2000

Publication series

NameProceedings of the Joint Conference on Information Sciences
Number2
Volume5

Other

OtherProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000
Country/TerritoryUnited States
CityAtlantic City, NJ
Period2/27/003/3/00

All Science Journal Classification (ASJC) codes

  • General Computer Science

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