Weili Yang
Junpei Wang
Yuan Zhou
Bin Geng
Georgios Kararigas
Jichun Yang
Qinghua Cui
aDepartment of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Center for Non-Coding RNA Medicine, Peking University, Beijing 100191, China
bSchool of Mathematics Sciences, Huaqiao University, Quanzhou 362021, China
cHypertension Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Beijing 100037, China
dCharité – Universit?tsmedizin Berlin, Corporate Member of Freie Universit?t Berlin, Humboldt-Universit?t zu Berlin, Berlin Institute of Health, Institute of Gender in Medicine and Center for Cardiovascular Research, DZHK (German Centre for Cardiovascular Research), 10115 Berlin, Germany
eCenter of Bioinformatics, Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
More InformationCorresponding author: E-mail address: yangj@bjmu.edu.cn (Jichun Yang);E-mail address: cuiqinghua@bjmu.edu.cn (Qinghua Cui)
Received Date: 2018-01-08
Accepted Date:2018-07-04
Rev Recd Date:2018-05-18
Available Online: 2018-07-24 Publish Date:2018-07-20
Abstract
Abstract
Enrichment analysis methods, e.g., gene set enrichment analysis, represent one class of important bioinformatical resources for mining patterns in biomedical datasets. However, tools for inferring patterns and rules of a list of drugs are limited. In this study, we developed a web-based tool, DrugPattern, for drug set enrichment analysis. We first collected and curated 7019 drug sets, including indications, adverse reactions, targets, pathways, etc. from public databases. For a list of interested drugs, DrugPattern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we employed it for the prediction of the effects of oxidized low-density lipoprotein (oxLDL), a factor expected to be deleterious. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by evidence in the literature. Because DrugPattern predicted the potential beneficial effects of oxLDL in type 2 diabetes (T2D), animal experiments were then performed to further verify this prediction. As a result, the experimental evidences validated the DrugPattern prediction that oxLDL indeed has beneficial effects on T2D in the case of energy restriction. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases. This study provides a tool to infer patterns and rules in biomedical datasets based on drug set enrichment analysis. DrugPattern is available at http://www.cuilab.cn/drugpattern.Keywords: Enrichment analysis,
Drug,
Data mining,
oxLDL,
Type 2 diabetes
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