Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

doi:10.1371/journal.pone.0078518

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	Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data
	Wang, Yongcui 1; Chen, Shilong1 ; Deng, Naiyang 2; Wang, Yong 3,4; Wang, Y (reprint author), Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing, Peoples R China.
	2013-11-11
发表期刊	PLOS ONE
ISSN	1932-6203
卷号	8 期号:11
文章类型	Article
摘要	Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems.; Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems.
WOS标题词	Science & Technology
DOI	10.1371/journal.pone.0078518
关键词[WOS]	PROTEIN INTERACTIONS ; PREDICTION ; SEQUENCE ; IDENTIFICATION ; KNOWLEDGEBASE ; SIMILARITY ; PROFILES ; MACHINE ; GENES
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(11201470 ; 31270270 ; 61171007 ; 11131009)
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000327221600028
引用统计	被引频次：103[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://210.75.249.4/handle/363003/3900
专题	中国科学院西北高原生物研究所
通讯作者	Wang, Y (reprint author), Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing, Peoples R China.
作者单位	1.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining, Peoples R China 2.China Agr Univ, Coll Sci, Beijing 100094, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing, Peoples R China 4.Natl Inst Adv Ind Sci & Technol, Mol Profiling Res Ctr Drug Discovery, Tokyo, Japan
推荐引用方式 GB/T 7714	Wang, Yongcui,Chen, Shilong,Deng, Naiyang,et al. Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data[J]. PLOS ONE,2013,8(11).
APA	Wang, Yongcui,Chen, Shilong,Deng, Naiyang,Wang, Yong,&Wang, Y .(2013).Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data.PLOS ONE,8(11).
MLA	Wang, Yongcui,et al."Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data".PLOS ONE 8.11(2013).

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