Prediction of network drug target based on improved model of bipartite graph valuation
Received:July 13, 2011  
DOI:10.4268/cjcmm20120201
KeyWord:bipartite graph  interaction  drug target  network pharmacology
Author NameAffiliationE-mail
LIU Xi Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China  
LU Peng Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Institute of Chinese Material Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China peng.lu@ia.ac.cn 
ZUO Xiaohan Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China  
CHEN Jianxin Beijing University of Traditional Chinese Medicine, Beijing 100029, China  
YANG Hongjun Institute of Chinese Material Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China  
YANG Yiping Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China  
GAO Yibo Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China yibo.gao@ia.ac.cn 
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Abstract:
      Network pharmacology, as a new developmental direction of drug discovery, was generating attention of more and more researchers. The key problem in drug discovery was how to identify the new interactions between drugs and target proteins. Prediction of new interaction was made to find potential targets based on the predicting model constructed by the known drug-protein interactions. According to the deficiencies of existing predicting algorithm based bipartite graph, a supervised learning integration method of bipartite graph was proposed in this paper. Firstly, the bipartite graph network was constructed based on the known interactions between drugs and target proteins. Secondly, the evaluation model for association between drugs and target proteins was created. Thirdly, the model was used to predict the new interactions between drugs and target proteins and confirm the new predicted targets. On the testing dataset, our method performed much better than three other predicting methods. The proposed method integrated chemical space, therapeutic space and genomic space, constructed the interaction network of drugs and target proteins, created the evaluation model and predicted the new interactions with good performance.
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