Poisson approach to clustering analysis of regulatory sequences.

Haiying Wang, Huiru Zheng, Takayuki Furuzuki

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

Fingerprint

Sequence Analysis
Cluster Analysis
Statistics
Clustering algorithms
Genes
Distance Education
Nucleic Acid Regulatory Sequences
Self organizing maps
Neural networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Drug Discovery

Cite this

Poisson approach to clustering analysis of regulatory sequences. / Wang, Haiying; Zheng, Huiru; Furuzuki, Takayuki.

In: International Journal of Computational Biology and Drug Design, Vol. 1, No. 2, 2008, p. 141-157.

Research output: Contribution to journalArticle

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