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BioMed Research International
Volume 2013 (2013), Article ID 132724, 6 pages
Research Article

Prediction of Drugs Target Groups Based on ChEBI Ontology

1Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
2College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
3Institute of Systems Biology, Shanghai University, Shanghai 200444, China
4Beijing Genomics Institute, Shenzhen Beishan Industrial Zone, Shenzhen 518083, China
5CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

Received 15 September 2013; Accepted 28 October 2013

Academic Editor: Tao Huang

Copyright © 2013 Yu-Fei Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset—the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups.