NWIPB OpenIR
Network predicting drug's anatomical therapeutic chemical code
Wang, Yong-Cui1; Chen, Shi-Long1; Deng, Nai-Yang2; Wang, Yong3,4; Wang, Y (reprint author), Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China.
2013-05-15
Source PublicationBIOINFORMATICS
ISSN1367-4803
Volume29Issue:10Pages:1317-1324
SubtypeArticle
AbstractMotivation: 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 HeadingsScience & Technology ; Life Sciences & Biomedicine ; Technology ; Physical Sciences
DOI10.1093/bioinformatics/btt158
WOS KeywordIDENTIFICATION ; CLASSIFICATION ; MACHINE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(11201470 ; 31270270 ; 61171007 ; 11131009)
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000319428200010
Citation statistics
Cited Times:31[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://210.75.249.4/handle/363003/3940
Collection中国科学院西北高原生物研究所
Corresponding AuthorWang, Y (reprint author), Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China.
Affiliation1.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
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
Network predicting d(389KB) 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Yong-Cui]'s Articles
[Chen, Shi-Long]'s Articles
[Deng, Nai-Yang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Yong-Cui]'s Articles
[Chen, Shi-Long]'s Articles
[Deng, Nai-Yang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Yong-Cui]'s Articles
[Chen, Shi-Long]'s Articles
[Deng, Nai-Yang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Network predicting drug's anatomical therapeutic chemical code.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.