期刊
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
卷 9, 期 1, 页码 111-129出版社
IMPERIAL COLLEGE PRESS
DOI: 10.1142/S0219720011005252
关键词
Biological relevance; Weighted Gene Co-Expression Network; functional module
类别
资金
- National Research Council of Thailand [PK/2551-212]
- National Research University
Relationships among gene expression levels may be associated with the mechanisms of the disease. While identifying a direct association such as a difference in expression levels between case and control groups links genes to disease mechanisms, uncovering an indirect association in the form of a network structure may help reveal the underlying functional module associated with the disease under scrutiny. This paper presents a method to improve the biological relevance in functional module identification from the gene expression microarray data by enhancing the structure of a weighted gene co-expression network using minimum spanning tree. The enhanced network, which is called a backbone network, contains only the essential structural information to represent the gene co-expression network. The entire backbone network is decoupled into a number of coherent sub-networks, and then the functional modules are reconstructed from these sub-networks to ensure minimum redundancy. The method was tested with a simulated gene expression dataset and case-control expression datasets of autism spectrum disorder and colorectal cancer studies. The results indicate that the proposed method can accurately identify clusters in the simulated dataset, and the functional modules of the backbone network are more biologically relevant than those obtained from the original approach.
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