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This article is part of the supplement: Advanced intelligent computing theories and their applications in bioinformatics. Proceedings of the 2011 International Conference on Intelligent Computing (ICIC 2011)

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Exploring drug combinations in genetic interaction network

Yin-Ying Wang12, Ke-Jia Xu12, Jiangning Song34 and Xing-Ming Zhao1*

Author affiliations

1 Institute of Systems Biology, Shanghai University, Shanghai 200444, China

2 Department of Mathematics, Shanghai University, Shanghai 200444, China

3 Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China

4 Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 7):S7  doi:10.1186/1471-2105-13-S7-S7

Published: 8 May 2012



Drug combination that consists of distinctive agents is an attractive strategy to combat complex diseases and has been widely used clinically with improved therapeutic effects. However, the identification of efficacious drug combinations remains a non-trivial and challenging task due to the huge number of possible combinations among the candidate drugs. As an important factor, the molecular context in which drugs exert their functions can provide crucial insights into the mechanism underlying drug combinations.


In this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations. Our results indicate that combinatorial drugs tend to have a smaller effect radius in the genetic interaction networks, which is an important parameter to describe the therapeutic effect of a drug combination from the network perspective. We also find that drug combinations are more likely to modulate functionally related pathways.


This study confirms that the molecular networks where drug combinations exert their functions can indeed provide important insights into the underlying rules of effective drug combinations. We hope that our findings can help shortcut the expedition of the future discovery of novel drug combinations.