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