College of Computer Science & Technology, Qingdao University, Qingdao 266071, China.
School of Automation, Qingdao University, Qingdao 266017, China.
Molecules. 2022 Aug 12;27(16):5131. doi: 10.3390/molecules27165131.
Nowadays, drug-target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug-target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge cannot make interactions prediction for under-studied drugs and targets. Hence, this article proposes a novel dual-network integrated logistic matrix factorization DTIs prediction scheme (Ro-DNILMF) via a knowledge graph embedding approach. This model adds prior knowledge as input data into the prediction model and inherits the advantages of the DNILMF model, which can predict under-studied drug-target interactions. Firstly, a knowledge graph embedding model based on relational rotation (RotatE) is trained to construct the interaction adjacency matrix and integrate prior knowledge. Secondly, a dual-network integrated logistic matrix factorization prediction model (DNILMF) is used to predict new drugs and targets. Finally, several experiments conducted on the public datasets are used to demonstrate that the proposed method outperforms the single base-line model and some mainstream methods on efficiency.
如今，药物-靶点相互作用（DTIs）预测是药物重定位的基础部分。然而，一方面，药物-靶点相互作用预测模型通常只考虑药物或靶点信息，而忽略了药物和靶点之间的先验知识。另一方面，包含先验知识的模型无法对研究较少的药物和靶点进行相互作用预测。因此，本文提出了一种通过知识图嵌入方法的新型双网络集成逻辑矩阵分解 DTIs 预测方案（Ro-DNILMF）。该模型将先验知识作为输入数据添加到预测模型中，并继承了 DNILMF 模型的优点，能够预测研究较少的药物-靶点相互作用。首先，训练基于关系旋转的知识图嵌入模型（RotatE），以构建交互邻接矩阵并集成先验知识。其次，使用双网络集成逻辑矩阵分解预测模型（DNILMF）来预测新的药物和靶点。最后，在公共数据集上进行了几项实验，以证明所提出的方法在效率方面优于单一基线模型和一些主流方法。
