Abstract—To identify liver cancer related genes and understand their interactions, we introduce a method based on literature mining. The genes are extracted with the disease-gene association classifier based on Bayesian network, and then the interaction network between the corresponding proteins is built. This paper has extracted 464 genes which are related to the liver cancer, and found genes such as p53, VEGF, TNF, AKT are hub proteins, which play important roles in the network. The KEGG pathway analysis shows that 19 enriched pathways may participate in liver carcinogenesis. Network analysis and pathway analysis implies the complexity of the occurrence, progression and prognosis of liver cancer.

Index Terms—Bayesian classifier, genes and pathways analysis, liver cancer, literature mining.

The authors are with the School of Electronic and Information Engineering, Soochow University, P. R. China (e-mail: liuxuan1220@ yahoo.cn, zouwei_198107@163.com, jjwang@suda.edu.cn).

 

Cite:Xuan Liu, Wei Zou, and Jiajun Wang, "Liver Cancer Related Gene Analysis Based on Literature Mining," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 3, no. 5, pp. 416-419, 2013.