Poisson approach to clustering analysis of regulatory sequences

Haiying Wang, Huiru Zheng*, Jinglu Hu

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

The presence of similar patterns in regulatory sequences may aid users in identifying co-regulated genes or inferring regulatory modules. By modelling pattern occurrences in regulatory regions with Poisson statistics, this paper presents a log likelihood ratio statistics-based distance measure to calculate pair-wise similarities between regulatory sequences. We employed it within three clustering algorithms: hierarchical clustering, Self-Organising Map, and a self-adaptive neural network. The results indicate that, in comparison to traditional clustering algorithms, the incorporation of the log likelihood ratio statistics-based distance into the learning process may offer considerable improvements in the process of regulatory sequence-based classification of genes.

本文言語English
ページ(範囲)141-157
ページ数17
ジャーナルInternational journal of computational biology and drug design
1
2
DOI
出版ステータスPublished - 2008 1月 1

ASJC Scopus subject areas

  • 創薬
  • コンピュータ サイエンスの応用

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