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The Prediction of Drug-Disease Correlation Based on Gene Expression Data

文献类型：期刊论文

作者Cui, Hui3,4,5; Zhang, Menghuan4,5; Yang, Qingmin5; Li, Xiangyi5; Liebman, Michael5; Xie, Lu5; Yang, Qingmin1; Liebman, Michael6; Yu, Ying2; ,
刊名BIOMED RESEARCH INTERNATIONAL
出版日期2018
卷号-期号:-页码:4028473
关键词C. elegans Chemicals Toxicity Image analysis Phenotype
ISSN号2314-6133
DOI10.1155/2018/4028473
文献子类Article
英文摘要The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.
学科主题Biotechnology & Applied Microbiology ; Research & Experimental Medicine
WOS关键词MELANOMA ; COMBINATIONS ; MODEL
语种英语
WOS记录号WOS:000428158700001
出版者HINDAWI LTD
版本出版稿
源URL[http://202.127.25.144/handle/331004/726]  
专题中国科学院上海生命科学研究院营养科学研究所
作者单位1.Shanghai Ocean Univ, Coll Food Sci & Technol, 999 Hu Cheng Huan Rd, Shanghai 201306, Peoples R China;
2.Tianjin Med Univ, Sch Basic Med Sci, Dept Pharmacol, Tianjin 30007, Peoples R China,
3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China;
4.Univ Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Nutr Sci, Shanghai 200031, Peoples R China;
5.Shanghai Acad Sci & Technol, Shanghai Ctr Bioinformat Technol, Shanghai 201203, Peoples R China;
6.IPQ Analyt LLC, Strateg Med, Philadelphia, PA USA;
推荐引用方式
GB/T 7714		 Cui, Hui,Zhang, Menghuan,Yang, Qingmin,et al. The Prediction of Drug-Disease Correlation Based on Gene Expression Data[J]. BIOMED RESEARCH INTERNATIONAL,2018,-(-):4028473.
APA Cui, Hui.,Zhang, Menghuan.,Yang, Qingmin.,Li, Xiangyi.,Liebman, Michael.,...&,.(2018).The Prediction of Drug-Disease Correlation Based on Gene Expression Data.BIOMED RESEARCH INTERNATIONAL,-(-),4028473.
MLA Cui, Hui,et al."The Prediction of Drug-Disease Correlation Based on Gene Expression Data".BIOMED RESEARCH INTERNATIONAL -.-(2018):4028473.

入库方式： OAI收割

来源：上海营养与健康研究所

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