IEEE J Biomed Health Inform. 2023 Jul;27(7):3686-3694. doi: 10.1109/JBHI.2023.3272154. Epub 2023 Jun 30.
Identifying drug-disease associations (DDAs) is critical to the development of drugs. Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is imperative to develop more accurate and effective methods for DDAs prediction. Most current DDAs prediction methods utilize original DDAs matrix directly. However, the original DDAs matrix is sparse, which greatly affects the prediction consequences. Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA) is proposed for DDAs prediction. First, MSGCA integrates multiple drug similarities and disease similarities using centered kernel alignment-based multiple kernel learning (CKA-MKL) algorithm to form new drug similarity and disease similarity, respectively. Second, the new drug and disease similarities are improved by linear neighborhood, and the DDAs matrix is reconstructed by weighted K nearest neighbor profiles. Next, the reconstructed DDAs and the improved drug and disease similarities are integrated into a heterogeneous network. Finally, the graph convolutional autoencoder with attention mechanism is utilized to predict DDAs. Compared with extant methods, MSGCA shows superior results on three datasets. Furthermore, case studies further demonstrate the reliability of MSGCA.
识别药物-疾病关联（DDAs）对于药物开发至关重要。传统的确定 DDAs 的方法既昂贵又低效。因此，迫切需要开发更准确、更有效的 DDAs 预测方法。目前大多数 DDAs 预测方法直接利用原始 DDAs 矩阵。然而，原始 DDAs 矩阵是稀疏的，这极大地影响了预测结果。因此，提出了一种基于多相似性图卷积自动编码器（MSGCA）的 DDAs 预测方法。首先，MSGCA 使用基于中心核对准的多核学习（CKA-MKL）算法整合多种药物相似性和疾病相似性，分别形成新的药物相似性和疾病相似性。其次，通过线性邻域对新的药物和疾病相似性进行改进，并通过加权 K 最近邻图重建 DDAs 矩阵。接下来，将重建的 DDAs 矩阵和改进的药物和疾病相似性整合到异构网络中。最后，利用具有注意力机制的图卷积自动编码器来预测 DDAs。与现有方法相比，MSGCA 在三个数据集上的表现都更为优异。此外，案例研究进一步证明了 MSGCA 的可靠性。
