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InFun: A method to detect overlapping gene communities by integrating gene expression and protein-protein interaction data
MIAO Qiumai,LU Xinguo *
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082
*Correspondence author
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Funding: National Natural Science Foundation of China（No.61502159, 61472467, 61672011）, Natural Science Foundation of Hunan Province（No.2018JJ2053）
Opened online:21 March 2019
Accepted by: none
Citation: MIAO Qiumai,LU Xinguo.InFun: A method to detect overlapping gene communities by integrating gene expression and protein-protein interaction data[OL]. [21 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4747845
 
 
The analysis of the gene expression data has great significance for clinical treatment, cancer diagnosis and other fields. Module network inference is an effective and established method to analyze the gene expression data. We hypothesized that exploring and analyzing the interaction between modules or no relationship modules will further improve our understanding of cancer mechanism. To this end, we proposed a novel method, InFun, for reconstruction different module networks. Our method applies two-ways clustering which contains Bayesian approach and overlapping community method to detect gene communities by integrating gene expression data and protein-protein interaction data. On the basis of The Cancer Genome Atlas (TCGA) breast cancer data, we observe that the InFun can recognize module networks which are significantly more enriched in the known pathways than another method like Lemon-Tree. These gene communities can serve as bio-markers to estimate the survival time of patients which is critical for cancer therapy. Discovery single function communities can predict breast cancer subtype by using different feature sets, and multiple function communities can communicate with other community which can be used to explain cancer processes. InFun brings new sight for understanding cancer machanism and novel technique for clustering gene expression data.
Keywords:module network; overlapping community; breast cancer
 
 
 

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