Identification of Lung Cancer Related Function Modules Based on Co-Expression Network
Abstract: Objective: Identifying lung cancer disease-related functional modules is important to understand the mechanism of lung cancer. Methods: In this paper, we propose an integration method of mining disease-related functional mod-ule. Using microarray data of normal and lung cancer samples, firstly, rank-based method was applied to construct gene co-expression network. Secondly, gene co-expression modules were mined through Qcut, then disease-related functional modules were screened based on the joint measure of lung cancer differentially expressed genes and the functional con-sistency. Results: 7 significant disease-related functional modules were screened, which were closely linked with the development of lung cancer by literature confirmation. Further it found that our method could not only return the func-tional consistency modules, but also find two modules were associated with specific functional annotations named “virus response” that could not be identified by other methods. Conclusions: The method provided additional insights for find-ing new functional module, which will be helpful for the studies on the pathogenesis of human complex diseases.
文章引用: 吕亚娜 , 何月涵 , 苗正强 , 贾婿 , 冯陈晨 , 陈丽娜 (2013) 基于共表达网络挖掘肺癌相关模块。 生物物理学， 1， 17-24. doi: 10.12677/biphy.2013.11003
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