合成致死（SL）是一种潜在的癌症治疗策略和药物发现方法。计算方法可以识别合成致死基因，这已成为湿实验的有效补充，因为湿实验耗时费力。图卷积网络（GCN）已被用于预测任务，并擅长于捕捉图中的邻居依赖关系。然而，还缺乏从各种异质图中汇总互补邻居信息的机制。因此，我们提出了多个关注图卷积网络（MAGCN）的合成致死预测。首先，我们从不同的数据源（如基因本体数据和蛋白质-蛋白质相互作用）分别获取基因的功能相似性特征和拓扑结构特征。然后，图卷积网络被用于根据合成致死关联积累邻居节点的知识。同时，我们提出了多个关注模型，并构建了多个关注网络来学习不同图的贡献因素，通过汇总这些图来生成内嵌表示。最后，生成的特征矩阵被解码以预测潜在的合成致死相互作用。实验结果表明，MAGCN优于其他基线方法。案例研究证明了MAGCN预测人类SL基因对的能力。

Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery. Computational approaches to identify synthetic lethality genes have become an effective complement to wet experiments which are time consuming and costly. Graph convolutional networks (GCN) has been utilized to such prediction task as be good at capturing the neighborhood dependency in a graph. However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs. Here, we propose the Multiple Attention Graph Convolution Networks for predicting synthetic lethality (MAGCN). First, we obtain the functional similarity features and topological structure features of genes from different data sources respectively, such as Gene Ontology data and Protein-Protein Interaction. Then, graph convolutional network is utilized to accumulate the knowledge from neighbor nodes according to synthetic lethal associations. Meanwhile, we propose a multiple graphs attention model and construct a multiple graphs attention network to learn the contribution factors of different graphs to generate embedded representation by aggregating these graphs. Finally, the generated feature matrix is decoded to predict potential synthetic lethal interaction. Experimental results show that MAGCN is superior to other baseline methods. Case study demonstrates the ability of MAGCN to predict human SL gene pairs.