TY - JOUR
T1 - Poisson approach to clustering analysis of regulatory sequences
AU - Wang, HY
AU - Zheng, H
AU - Jinglu, Hu
PY - 2008/9
Y1 - 2008/9
N2 - 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
AB - 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
UR - http://www.inderscience.com/browse/index.php
U2 - 10.1504/IJCBDD.2008.020206
DO - 10.1504/IJCBDD.2008.020206
M3 - Article
SN - 1756-0764
VL - 1
SP - 141
EP - 157
JO - International Journal of Computational Biology and Drug Design
JF - International Journal of Computational Biology and Drug Design
IS - 2
ER -