Poisson approach to clustering analysis of regulatory sequences

Haiying Wang, Huiru Zheng, Jinglu Hu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)141-157
Number of pages17
JournalInternational journal of computational biology and drug design
Volume1
Issue number2
DOIs
Publication statusPublished - 2008 Jan 1

Keywords

  • Poisson distribution
  • hierarchical clustering
  • log likelihood ratio statistics
  • neural networks
  • regulatory sequence

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications

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