Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data.
Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data.
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DOI：
10.1142/9789814583220_0015
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发表时间：
2013-11
影响因子：
--
通讯作者：
Fan Yang;Jinbo Xu;Jianyang Zeng
中科院分区：
文献类型：
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作者：
Fan Yang;Jinbo Xu;Jianyang Zeng
计算机模拟预测未知药物-靶标相互作用（DTI）已成为药物重新定位和药物开发的常用工具。DTI预测的一个关键挑战在于整合多种类型的数据以进行准确的DTI预测。虽然最近的研究表明，基因组，化学和药理学数据可以为DTI预测提供可靠的信息，但尚不清楚蛋白质的功能信息是否也有助于这项任务。几乎没有开展联合收割机将这些信息与其他数据结合起来，以确定药物和靶点之间的新相互作用。本文将功能数据引入到DTI预测中，并利用功能相似性度量为目标构建生物空间。我们提出了一个概率图形模型，称为条件随机场（CRF），系统地整合基因组，化学，功能和药理学数据加上拓扑结构的DTI网络到一个统一的框架来预测丢失的DTI。在两个基准数据集上的测试表明，该方法具有良好的预测性能，精确率-召回率曲线下面积（AUPR）高达94.9。这些结果表明，我们的CRF模型可以成功地利用异质数据来捕获DTI的潜在相关性，因此对于药物重新定位将具有实际用途。补充材料可在http://iiis.tsinghua.edu.cn/~compbio/papers/psb2014/psb2014_sm.pdf上获得。
In silico prediction of unknown drug-target interactions (DTIs) has become a popular tool for drug repositioning and drug development. A key challenge in DTI prediction lies in integrating multiple types of data for accurate DTI prediction. Although recent studies have demonstrated that genomic, chemical and pharmacological data can provide reliable information for DTI prediction, it remains unclear whether functional information on proteins can also contribute to this task. Little work has been developed to combine such information with other data to identify new interactions between drugs and targets. In this paper, we introduce functional data into DTI prediction and construct biological space for targets using the functional similarity measure. We present a probabilistic graphical model, called conditional random field (CRF), to systematically integrate genomic, chemical, functional and pharmacological data plus the topology of DTI networks into a unified framework to predict missing DTIs. Tests on two benchmark datasets show that our method can achieve excellent prediction performance with the area under the precision-recall curve (AUPR) up to 94.9. These results demonstrate that our CRF model can successfully exploit heterogeneous data to capture the latent correlations of DTIs, and thus will be practically useful for drug repositioning. Supplementary Material is available at http://iiis.tsinghua.edu.cn/~compbio/papers/psb2014/psb2014_sm.pdf.