A weighted structural model clustering approach for identifying and analyzing core genetic regulatory modules

Core regulatory modules play fundamental roles in amassing, processing and dispatching genetic information during whole cell life cycle. Currently most clustering methods fail to abstract inherent biological contents from related high-throughput expression profiles, although they may reduce high dimension to a certain low one. The work proposes a weighted structural model clustering method for integrative detection and analysis of core regulatory modules. The experiments on diverse data sources prove it can predict core regulatory modules effectively, thus it constructs a valuable perspective and unique measure for vital topics as pathway detection, quantitative reconstruction of bio-networks, and novel drug discovery in systems biology and bioinformatics, especially for large-scale dynamic systems and expression profiles with consideration of inherent biological meanings.