Journal of Applied Mathematics
Volume 2012 (2012), Article ID 248658, 21 pages
Research Article

Modules Identification in Gene Positive Networks of Hepatocellular Carcinoma Using Pearson Agglomerative Method and Pearson Cohesion Coupling Modularity

1School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
2School of Computing, Engineering and Information Sciences, Northumbria University, Newcastle Upon Tyne NE1 8ST, UK

Received 5 June 2012; Accepted 26 June 2012

Academic Editor: Dexing Kong

Copyright © 2012 Jinyu Hu and Zhiwei Gao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this study, a gene positive network is proposed based on a weighted undirected graph, where the weight represents the positive correlation of the genes. A Pearson agglomerative clustering algorithm is employed to build a clustering tree, where dotted lines cut the tree from bottom to top leading to a number of subsets of the modules. In order to achieve better module partitions, the Pearson correlation coefficient modularity is addressed to seek optimal module decomposition by selecting an optimal threshold value. For the liver cancer gene network under study, we obtain a strong threshold value at 0.67302, and a very strong correlation threshold at 0.80086. On the basis of these threshold values, fourteen strong modules and thirteen very strong modules are obtained respectively. A certain degree of correspondence between the two types of modules is addressed as well. Finally, the biological significance of the two types of modules is analyzed and explained, which shows that these modules are closely related to the proliferation and metastasis of liver cancer. This discovery of the new modules may provide new clues and ideas for liver cancer treatment.