Network predicting drug's anatomical therapeutic chemical code

doi:10.1093/bioinformatics/btt158

NWIPB OpenIR

	Network predicting drug's anatomical therapeutic chemical code
	Wang, Yong-Cui 1; Chen, Shi-Long 1; Deng, Nai-Yang 2; Wang, Yong 3,4; Wang, Y (reprint author), Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China.
	2013-05-15
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	29 期号:10 页码:1317-1324
文章类型	Article
摘要	Motivation: Discovering drug's Anatomical Therapeutic Chemical (ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action. However, few studies attempt to annotate drug's potential ATC-codes by computational approaches. Results: Here, we introduce drug-target network to computationally predict drug's ATC-codes and propose a novel method named NetPredATC. Starting from the assumption that drugs with similar chemical structures or target proteins share common ATC-codes, our method, NetPredATC, aims to assign drug's potential ATC-codes by integrating chemical structures and target proteins. Specifically, we first construct a gold-standard positive dataset from drugs' ATC-code annotation databases. Then we characterize ATC-code and drug by their similarity profiles and define kernel function to correlate them. Finally, we use a kernel method, support vector machine, to automatically predict drug's ATC-codes. Our method was validated on four drug datasets with various target proteins, including enzymes, ion channels, G-protein couple receptors and nuclear receptors. We found that both drug's chemical structure and target protein are predictive, and target protein information has better accuracy. Further integrating these two data sources revealed more experimentally validated ATC-codes for drugs. We extensively compared our NetPredATC with SuperPred, which is a chemical similarity-only based method. Experimental results showed that our NetPredATC outperforms SuperPred not only in predictive coverage but also in accuracy. In addition, database search and functional annotation analysis support that our novel predictions are worthy of future experimental validation. Conclusion: In conclusion, our new method, NetPredATC, can predict drug's ATC-codes more accurately by incorporating drug-target network and integrating data, which will promote drug mechanism understanding and drug repositioning and discovery.; Motivation: Discovering drug's Anatomical Therapeutic Chemical (ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action. However, few studies attempt to annotate drug's potential ATC-codes by computational approaches.
WOS标题词	Science & Technology ; Life Sciences & Biomedicine ; Technology ; Physical Sciences
DOI	10.1093/bioinformatics/btt158
关键词[WOS]	IDENTIFICATION ; CLASSIFICATION ; MACHINE
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	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000319428200010
引用统计	被引频次：39[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/363003/3940
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, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China.
作者单位	1.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining 810001, Peoples R China 2.China Agr Univ, Coll Sci, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yong-Cui,Chen, Shi-Long,Deng, Nai-Yang,et al. Network predicting drug's anatomical therapeutic chemical code[J]. BIOINFORMATICS,2013,29(10):1317-1324.
APA	Wang, Yong-Cui,Chen, Shi-Long,Deng, Nai-Yang,Wang, Yong,&Wang, Y .(2013).Network predicting drug's anatomical therapeutic chemical code.BIOINFORMATICS,29(10),1317-1324.
MLA	Wang, Yong-Cui,et al."Network predicting drug's anatomical therapeutic chemical code".BIOINFORMATICS 29.10(2013):1317-1324.

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