Paper Info
Title
Identifying Biomarkers of Hepatocellular Carcinoma Based on Gene Co-Expression Network from High-Throughput Data.
Abstract
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 Kruppel -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 verb the accuracy of the classification
Year
DOI
Venue
2017
10.3233/978-1-61499-830-3-667
Studies in Health Technology and Informatics
Keywords
Field
DocType
Hepatocellular Carcinoma,Cluster Analysis,Machine Learning
Hepatocellular carcinoma,Biology,Biomarker (medicine),Throughput,Gene co-expression network,Computational biology
Conference
Volume
ISSN
Citations 
245
0926-9630
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Ying Zhang162594.27
Zhiping Liu200.34
Jing-Song Li322211.02