Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai 200240, China.
Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of informatics, Huazhong Agricultural University, Wuhan 430070, China.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac403.
Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.
由于组合的复杂性以及 SDC 是细胞系特异性的事实，识别协同药物组合 (SDC) 是一项巨大的挑战。现有的计算方法要么没有考虑 SDC 的细胞系特异性，要么通过为每个细胞系独立构建模型而表现不佳。在本文中，我们提出了一种名为 SDCNet 的新型编码器-解码器网络，用于预测细胞系特异性 SDC。SDCNet 在一个模型中学习不同细胞系之间的共同模式以及细胞系特异性的药物组合特征。这是通过将不同细胞系的 SDC 图视为关系图，并构建关系图卷积网络 (R-GCN) 作为编码器来实现的，以学习和融合不同细胞系的药物的深度表示。设计了一种注意力机制来根据药物的相对重要性，从 R-GCN 的不同层集成药物特征，从而进一步增强表示学习。通过在细胞系特异性解码器中共享部分参数来利用共同模式，不仅可以重建已知的 SDC，还可以为每个细胞系预测新的 SDC。在各种数据集上的实验表明，SDCNet 优于最先进的方法，并且在推广到与训练数据不同的新细胞系时也具有鲁棒性。最后，案例研究再次证实了我们的方法在预测新型可靠的细胞系特异性 SDC 方面的有效性。
