The full text of this article
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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