title: Poisson approach to clustering analysis of regulatory sequences.
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.
authors.affiliation: School of Computing and Mathematics, University of Ulster at Jordanstown, N. Ireland, UK. hy.wang@ulster.ac.uk
authors.forename: Haiying
authors.initials: H
authors.lastname: Wang
authors.forename: Huiru
authors.initials: H
authors.lastname: Zheng
authors.forename: Jinglu
authors.initials: J
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D016000:Cluster Analysis; D016013:Likelihood Functions; D016571:Neural Networks, Computer; D016012:Poisson Distribution; D012045:Regulatory Sequences, Nucleic Acid