Drug-target interactions prediction using marginalized denoising model on heterogeneous networks.
Drug-target interactions prediction using marginalized denoising model on heterogeneous networks.
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DOI：
10.1186/s12859-020-03662-8
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发表时间：
2020-07-23
影响因子：
3
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Tang C;Zhong C;Chen D;Wang J
药物通过作用于靶蛋白质实现药理学功能。确定药物与靶蛋白之间的相互作用是旧药物重新定位和新药发现的重要任务。为了推荐新的候选药物和重新定位现有药物，通常采用计算方法。与湿实验室实验相比，计算方法具有更低的药物发现成本，并为后续的实验验证提供了有效的指导。如何整合不同类型的生物数据和处理药物-靶标相互作用数据的稀疏性仍然是一个巨大的挑战。在本文中，我们提出了一种新的药物-靶标相互作用（DTI）预测方法，结合边缘化去噪模型的关联指数核矩阵和潜在的全球关联异构网络。在基准数据集和新编译数据集上的实验结果表明，与现有方法相比，该方法具有更高的AUC（area under curve of receiver operating characteristic）和更大的AUPR（area under curve of precision-recall curve）。在我们的方法的性能改善依赖于关联索引核矩阵和潜在的全球关联。关联指数核矩阵计算药物和靶标之间的共享关系。潜在的全局关联解决了由网络链路稀疏性引起的误报问题。我们的方法可以提供一个有用的方法来推荐新的候选药物和重新定位现有的药物。
Drugs achieve pharmacological functions by acting on target proteins. Identifying interactions between drugs and target proteins is an essential task in old drug repositioning and new drug discovery. To recommend new drug candidates and reposition existing drugs, computational approaches are commonly adopted. Compared with the wet-lab experiments, the computational approaches have lower cost for drug discovery and provides effective guidance in the subsequent experimental verification. How to integrate different types of biological data and handle the sparsity of drug-target interaction data are still great challenges. In this paper, we propose a novel drug-target interactions (DTIs) prediction method incorporating marginalized denoising model on heterogeneous networks with association index kernel matrix and latent global association. The experimental results on benchmark datasets and new compiled datasets indicate that compared to other existing methods, our method achieves higher scores of AUC (area under curve of receiver operating characteristic) and larger values of AUPR (area under precision-recall curve). The performance improvement in our method depends on the association index kernel matrix and the latent global association. The association index kernel matrix calculates the sharing relationship between drugs and targets. The latent global associations address the false positive issue caused by network link sparsity. Our method can provide a useful approach to recommend new drug candidates and reposition existing drugs.
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