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Poisson approach to clustering analysis of regulatory sequences

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wang, HY, Zheng, H and Jinglu, Hu (2008) Poisson approach to clustering analysis of regulatory sequences. International Journal of Computational Biology and Drug Design (IJCBDD), 1 (2). pp. 141-157. [Journal article]

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DOI: 10.1504/IJCBDD.2008.020206

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

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:8837
Deposited By:Dr Haiying Wang
Deposited On:20 Jan 2010 16:07
Last Modified:20 Jan 2010 16:07

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