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Poisson approach to clustering analysis of regulatory sequences
by Haiying Wang, Huiru Zheng, Jinglu Hu
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 1, No. 2, 2008

 

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.

 

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