Drug-target interaction prediction by random walk on the heterogeneous network
Drug-target interaction prediction by random walk on the heterogeneous network
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DOI：
10.1039/c2mb00002d
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发表时间：
2012-01-01
影响因子：
--
通讯作者：
Yan, Gui-Ying
中科院分区：
文献类型：
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作者：
Chen, Xing;Liu, Ming-Xi;Yan, Gui-Ying
从异质生物学数据中预测潜在的药物-靶标相互作用不仅对于更好地理解各种相互作用和生物学过程至关重要，而且对于新药的开发和人类药物的改进也至关重要。本文提出了基于异质网络的重启随机游走（NRWRH）方法，在相似药物往往靶向相似靶蛋白的假设下，利用随机游走的框架，对潜在的药物-靶相互作用进行大规模预测。与传统的监督或半监督方法相比，NRWRH充分利用网络工具进行数据集成，预测药物-靶标关联。它通过已知的药物-靶标相互作用将蛋白质-蛋白质相似性网络、药物-药物相似性网络和已知的药物-靶标相互作用网络三种不同的网络集成为一个异构网络，并在这个异构网络上实现随机游走。当应用于四类重要的药物-靶标相互作用时，包括酶，离子通道，GPCR和核受体，NRWRH在交叉验证和潜在的药物-靶标相互作用预测方面显着改进了以前的方法。优异的性能使我们能够为药物开发提出许多新的潜在药物-靶标相互作用。
Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.