Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving
Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving
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DOI：
10.1093/bib/bbac059
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发表时间：
2022-03-09
影响因子：
9.5
通讯作者：
Sakurai, Tetsuya
中科院分区：
文献类型：
--
作者：
Shang, Yifan;Ye, Xiucai;Sakurai, Tetsuya
准确预测药物-靶标相互作用（DTI）可以减少药物重新定位和药物发现的成本和时间。目前的许多方法集成了来自药物和靶标的多个数据源的信息，以提高DTI预测的准确性。然而，这些方法没有考虑不同数据源之间的复杂关系。在这项研究中，我们提出了一种新的计算框架，称为MccDTI，预测潜在的DTI的多视图网络嵌入，它可以集成的异质性信息的药物和目标。MccDTI通过保持多视图网络之间的一致性和互补性信息来学习药物和靶标的高质量低维表示。然后MccDTI采用基于药物和靶点表征的矩阵补全方案进行DTI预测。在两个数据集上的实验结果表明，MccDTI的预测精度优于四种最先进的DTI预测方法。此外，文献验证的DTI预测表明，MccDTI可以预测可靠的潜在DTI。这些结果表明，MccDTI可以提供一个强大的工具，预测新的DTI和加速药物发现。
Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery.