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	Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources
	Liu, Zhongyang ; Guo, Feifei ; Gu, Jiangyong ; Wang, Yong ; Li, Yang ; Wang, Dan ; Lu, Liang ; Li, Dong ; He, Fuchu
	2015
关键词	INTERACTION NETWORKS INFORMATION METABOLISM PROTEINS
英文摘要	Motivation: Anatomical Therapeutic Chemical (ATC) classification system, widely applied in almost all drug utilization studies, is currently the most widely recognized classification system for drugs. Currently, new drug entries are added into the system only on users' requests, which leads to seriously incomplete drug coverage of the system, and bioinformatics prediction is helpful during this process. Results: Here we propose a novel prediction model of drug-ATC code associations, using logistic regression to integrate multiple heterogeneous data sources including chemical structures, target proteins, gene expression, side-effects and chemical-chemical associations. The model obtains good performance for the prediction not only on ATC codes of unclassified drugs but also on new ATC codes of classified drugs assessed by cross-validation and independent test sets, and its efficacy exceeds previous methods. Further to facilitate the use, the model is developed into a user-friendly web service SPACE (Similarity-based Predictor of ATC CodE), which for each submitted compound, will give candidate ATC codes (ranked according to the decreasing probability_score predicted by the model) together with corresponding supporting evidence. This work not only contributes to knowing drugs' therapeutic, pharmacological and chemical properties, but also provides clues for drug repositioning and side-effect discovery. In addition, the construction of the prediction model also provides a general framework for similarity-based data integration which is suitable for other drug-related studies such as target, side-effect prediction etc.; National Key Technology RD Program [2012BAI29B07]; Program of International ST Cooperation [2014DFB30020]; Chinese High Technology Research and Development [2012AA020201]; SCI(E); CPCI-S(ISTP); 11; 1788-1795; 31
语种	英语
出处	11th ISCB-Student-Council Symposium held in conjunction with the 23rd Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) / 14th European Conference on Computational Biology (ECCB)
DOI标识	10.1093/bioinformatics/btv055
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/493673]
专题	化学与分子工程学院
推荐引用方式 GB/T 7714	Liu, Zhongyang,Guo, Feifei,Gu, Jiangyong,et al. Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources. 2015-01-01.

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