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Finding Consensus Patterns in Very Scarce Biosequence Samples from Their Minimal Multiple Generalizations

  • Yen Kaow Ng
  • Takeshi Shinohara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

In this paper we examine the issues involved in finding consensus patterns from biosequence data of very small sample sizes, by searching for so-called minimal multiple generalization (mmg), that is, a set of syntactically minimal patterns that accounts for all the samples. The data we use are the sigma regulons with more conserved consensus patterns for the bacteria B. subtilis. By comparing between the mmgs found over different search spaces, we found that it is possible to derive patterns close to the known consensus patterns by simply making some reasonable requirements on the kinds of patterns to obtain. We also propose some simple measures to evaluate the patterns in an mmg.

Keywords

Regular Pattern Pattern Class Pattern Language Variable Occurrence Consensus Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yen Kaow Ng
    • 1
  • Takeshi Shinohara
    • 2
  1. 1.Graduate School of Computer Science and SystemsKyushu Institute of TechnologyIizukaJapan
  2. 2.Department of Artificial IntelligenceKyushu Institute of TechnologyIizukaJapan

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