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This page (revision-5) was last changed on 29-Jul-2014 11:01 by YongWang

This page was created on 12-Oct-2013 14:57 by YongWang

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* Lin Wang, Yong Wang, Qinghua Hu and Shao Li. Exploring drug-gene-disease coherent subnetworks for revealing drug-disease associations. In submission.
* Lin Wang, Yong Wang, Qinghua Hu and Shao Li. Systematic analysis of new drug indications by drug-gene-disease coherent subnetworks. In submission.
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How to analyze transcriptional response data to identify meaningful gene modules shared by both drugs and diseases is still a critical issue to elucidate the molecular basis of drug-disease associations.
Drug targets and disease genes may work as driver factors in transcriptional level, which propagate signals through gene regulatory network and cause the downstream genes' differential expression. How to analyze transcriptional response data to identify meaningful gene modules shared by both drugs and diseases is still a critical issue for molecular basis of drug-disease associations.
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Here, we propose the drug-gene-disease coherent subnetwork concept to group the biological function related drugs, diseases and genes. Based on disease-related and drug-induced transcriptional response profiles, we develop a multiple non-negative matrix factorization method to identify drug-gene-disease coherent subnetworks. Additional heterogeneous data are simultaneously integrated in a regularized manner. Meanwhile, the sparsity penalties are employed to achieve modular solutions.
Here, we propose the drug-gene-disease coherent subnetwork concept to group the biological function related drugs, diseases, and genes. It was defined as the subnetwork with drug, gene, and disease as nodes and their interactions coherently crossing three layers as edges. Based on differential expression profiles of 418 drugs and 84 diseases, we develop a computational framework and identify 13 coherent subnetworks such as inflammatory bowel disease melanoma and melanoma inflammatory bowel disease relevant subnetwork. The results demonstrate that our coherent subnetwork approach is able to identify novel drug indications and highlight their molecular basis.
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