Zhang Xin, Li Limin, Ng Michael K, Zhang Shuqin
School of Mathematical Sciences, Fudan University, Shanghai 200433, China.
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China.
Comput Biol Chem. 2017 Aug;69:185-193. doi: 10.1016/j.compbiolchem.2017.03.011. Epub 2017 Mar 31.
Drug-target interaction (DTI) prediction is a challenging step in further drug repositioning, drug discovery and drug design. The advent of high-throughput technologies brings convenience to the development of DTI prediction methods. With the generation of a high number of data sets, many mathematical models and computational algorithms have been developed to identify the potential drug-target pairs. However, most existing methods are proposed based on the single view data. By integrating the drug and target data from different views, we aim to get more stable and accurate prediction results. In this paper, a multiview DTI prediction method based on clustering is proposed. We first introduce a model for single view drug-target data. The model is formulated as an optimization problem, which aims to identify the clusters in both drug similarity network and target protein similarity network, and at the same time make the clusters with more known DTIs be connected together. Then the model is extended to multiview network data by maximizing the consistency of the clusters in each view. An approximation method is proposed to solve the optimization problem. We apply the proposed algorithms to two views of data. Comparisons with some existing algorithms show that the multiview DTI prediction algorithm can produce more accurate predictions. For the considered data set, we finally predict 54 possible DTIs. From the similarity analysis of the drugs/targets, enrichment analysis of DTIs and genes in each cluster, it is shown that the predicted DTIs have a high possibility to be true.
药物-靶点相互作用（DTI）预测是进一步进行药物重新定位、药物发现和药物设计中的一个具有挑战性的步骤。高通量技术的出现为DTI预测方法的发展带来了便利。随着大量数据集的产生，已经开发了许多数学模型和计算算法来识别潜在的药物-靶点对。然而，大多数现有方法是基于单视图数据提出的。通过整合来自不同视图的药物和靶点数据，我们旨在获得更稳定和准确的预测结果。本文提出了一种基于聚类的多视图DTI预测方法。我们首先介绍了一种单视图药物-靶点数据模型。该模型被表述为一个优化问题，旨在识别药物相似性网络和靶点蛋白质相似性网络中的聚类，同时使具有更多已知DTI的聚类连接在一起。然后通过最大化每个视图中聚类的一致性将该模型扩展到多视图网络数据。提出了一种近似方法来解决该优化问题。我们将所提出的算法应用于两个视图的数据。与一些现有算法的比较表明，多视图DTI预测算法可以产生更准确的预测。对于所考虑的数据集，我们最终预测了54个可能的DTI。从药物/靶点的相似性分析、每个聚类中DTI和基因的富集分析来看，预测的DTI很有可能是真实的。
