Exploring drug combinations in a drug-cocktail network

Ke-Jia Xu, Fu-Yan Hu, Jiangning Song, Xing-Ming Zhao

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

8 Citations (Scopus)


Combination of different agents is widely used clinically to combat complex diseases with improved therapy and decreased side effects. It is necessary to understand the underlying mechanisms of drug combinations. In this work, we proposed a network-based approach to investigate drug combinations. Our results showed that the agents in an effective combination tend to have more similar therapeutic effects and more interaction partners in a drug-cocktail networks than random combination networks. Based on our results, we further developed a statistical model termed as Drug Combination Predictor (DCPred) by using the topological features of the drug-cocktail network, and assessed its prediction performance by making full use of a well-prepared dataset containing all known effective drug combinations extracted from the Drug Combination Database (DCDB). As a result, our model achieved the overall best AUC (Area Under the Curve) score of 0.92. Our findings provide useful insights into the underlying rules of effective drug combinations and offer important clues as to how to accelerate the discovery process of new combination drugs in the future.
Original languageEnglish
Title of host publication2011 IEEE Conference on Systems Biology (ISB)
EditorsL Chen, X S Zhang, Y Wang
Place of PublicationChina
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages382 - 387
Number of pages6
ISBN (Print)9781457716669
Publication statusPublished - 2011
EventInternational Conference on Computational Systems Biology (ISB) 2011 - Zhuhai, China
Duration: 2 Sep 20114 Sep 2011
Conference number: 5th


ConferenceInternational Conference on Computational Systems Biology (ISB) 2011
Abbreviated titleISB 2011

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