Short segment frequency equalization: A simple and effective alternative treatment of background models in motif discovery

Kazuhito Shida

研究成果: Conference contribution

抄録

One of the most important pattern recognition problems in bioinformatics is the de novo motif discovery. In particular, there is a large room of improvement in motif discovery from eukaryotic genome, where the sequences have complicated background noise. The short segment frequency equalization (SSFE) is a novel treatment method to incorporate Markov background models into de novo motif discovery algorithms, namely Gibbs sampling. Despite its apparent simplicity, SSFE shows a large performance improvement over the current method (Q/P scheme) when tested on artificial DNA datasets with Markov background of human and mouse. Furthermore, SSFE shows a better performance than other methods including much more complicated and sophisticated method, Weeder 1.3, when tested with several biological datasets from human promoters.

本文言語English
ホスト出版物のタイトルPattern Recognition in Bioinformatics - 4th IAPR International Conference, PRIB 2009, Proceedings
出版社Springer Verlag
ページ355-364
ページ数10
ISBN(印刷版)3642040306, 9783642040306
DOI
出版ステータスPublished - 2009
イベント4th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2009 - Sheffield, United Kingdom
継続期間: 2009 9月 72009 9月 9

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5780 LNBI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other4th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2009
国/地域United Kingdom
CitySheffield
Period09/9/709/9/9

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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