Title :
Hierarchical modular structure in gene coexpression networks
Author_Institution :
Center for Comput. Syst. Biol., Fudan Univ., Shanghai, China
Abstract :
Network module (community) structure has been a hot research topic in recent years. Many methods have been proposed for module detection and identification. Hierarchical structure of modules is shown to exist in different kinds of biological networks. Compared to the module identification methods, less research is done on the hierarchical structure of modules. In this paper, we propose a method for constructing the hierarchical modular structure in networks based on the extended random graph model. Statistical tests are applied to test the hierarchical relations between different modules. We give both artificial networks and real data examples to illustrate the performance of our approach. Application of the proposed method to yeast gene co-expression network shows that it does have a hierarchical modular structure with the modules on different levels corresponding to different gene functions.
Keywords :
complex networks; genetics; graph theory; random processes; biological network; extended random graph model; gene coexpression network; hierarchical modular structure; module detection; module identification; network community structure; network module structure; statistical test; Clustering algorithms; Conferences; Educational institutions; Partitioning algorithms; Probability; Shape; Systems biology;
Conference_Titel :
Systems Biology (ISB), 2012 IEEE 6th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4673-4396-1
Electronic_ISBN :
978-1-4673-4397-8
DOI :
10.1109/ISB.2012.6314123