Identifying Biomarkers of Hepatocellular Carcinoma Based on Gene Co-Expression Network from High-Throughput Data
In this paper,we proposed an approach systematically based on the use of gene co-expression network analyses to identify potential biomarkers for Hepatocellular Carcinoma(HCC).With the analysis of differential gene expression,we first selected candidate genes closely related to HCC from the whole genome on a large scale.By identifying the relationships between each two genes,we built up the gene co-expression network using Cytoscape software.Then the global network was clustered into several sub-modules by Markov Cluster Algorithm(MCL).And,GO-Analysis was carried out for these identified gene modules to further explore the genes obviously associated with the dysfunctions of HCC,and in result we find Hexokinase 2(HK2)and Krüppel-like Factor 4(KLF4)as potential candidate biomarkers to provide insights into the mechanism of the development of HCC.Finally,we evaluated the disease classification results via an SVM-based machine learning method to verify the accuracy of the classification.
Hepatocellular Carcinoma Cluster Analysis Machine Learning
Ying Zhang Zhiping Liu Jing-song Li
Engineering Research Center of EMR and Intelligent Expert System,Ministry of Education,Collaborative Biomedical Engineering,College of Control Science and Engineering,Shandong University,Jinan,Shandong Engineering Research Center of EMR and Intelligent Expert System,Ministry of Education,Collaborative
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
667-671
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）