Poisson approach to clustering analysis of regulatory sequences

HY Wang, H Zheng, Hu Jinglu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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
JournalInternational Journal of Computational Biology and Drug Design
Issue number2
Publication statusPublished (in print/issue) - Sept 2008


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