A Computational Study Identifies HIV Progression-Related Genes Using mRMR and Shortest Path Tracing |
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| Authors: | Chengcheng Ma Xiao Dong Rudong Li Lei Liu |
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| Institution: | 1. Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China.; 2. University of Chinese Academy of Sciences, Beijing, P.R. China.; 3. Shanghai Center for Bioinformation Technology, Shanghai, P.R. China.; 4. Institutes for Biomedical Sciences, Fudan University, Shanghai, P.R. China.; George Mason University, United States of America, |
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| Abstract: | Since statistical relationships between HIV load and CD4+ T cell loss have been demonstrated to be weak, searching for host factors contributing to the pathogenesis of HIV infection becomes a key point for both understanding the disease pathology and developing treatments. We applied Maximum Relevance Minimum Redundancy (mRMR) algorithm to a set of microarray data generated from the CD4+ T cells of viremic non-progressors (VNPs) and rapid progressors (RPs) to identify host factors associated with the different responses to HIV infection. Using mRMR algorithm, 147 gene had been identified. Furthermore, we constructed a weighted molecular interaction network with the existing protein-protein interaction data from STRING database and identified 1331 genes on the shortest-paths among the genes identified with mRMR. Functional analysis shows that the functions relating to apoptosis play important roles during the pathogenesis of HIV infection. These results bring new insights of understanding HIV progression. |
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