癌症是一种多方面的疾病，由于多个生物分子的共同突变所致。癌症治疗的一个有前景的策略涉及利用合成致死（SL）现象，通过靶向癌基因的SL伴侣来进行治疗。由于传统的SL预测方法成本高、耗时长且产生错误的效应靶位，计算方法已成为其有效补充。现有方法中，大多数将SL关联独立于其他生物交互网络，并未考虑来自各种生物网络的其他信息。尽管一些方法已整合了不同网络以捕捉基因的多模态特征来预测SL，但这些方法隐含地假设所有信息源和层次对SL关联的贡献相等。因此，目前仍缺乏一种全面且灵活的学习基因跨网络表示用于SL预测的框架。在本研究中，我们提出了一种新颖的同时注意跨网络表示学习方法（TARSL），通过捕捉来自异质源的分子特征。我们采用三级注意模块来考虑多层次信息的不同贡献。具体而言，特征级别的注意力能够捕捉分子特征和网络连接之间的相关性，节点级别的注意力能够区分各种邻居的重要性，网络级别的注意力能够集中于重要的网络，并减少无关网络的影响。我们对人类SL数据集进行了全面的实验，结果证明我们的模型一致优于基准方法，并且预测的SL关联可以帮助设计抗癌药物。

Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic Lethality (SL) by targeting the SL partner of cancer gene. Since traditional methods for SL prediction suffer from high-cost, time-consuming and off-targets effects, computational approaches have been efficient complementary to these methods. Most of existing approaches treat SL associations as independent of other biological interaction networks, and fail to consider other information from various biological networks. Despite some approaches have integrated different networks to capture multi-modal features of genes for SL prediction, these methods implicitly assume that all sources and levels of information contribute equally to the SL associations. As such, a comprehensive and flexible framework for learning gene cross-network representations for SL prediction is still lacking. In this work, we present a novel Triple-Attention cross-network Representation learning for SL prediction (TARSL) by capturing molecular features from heterogeneous sources. We employ three-level attention modules to consider the different contribution of multi-level information. In particular, feature-level attention can capture the correlations between molecular feature and network link, node-level attention can differentiate the importance of various neighbors, and network-level attention can concentrate on important network and reduce the effects of irrelated networks. We perform comprehensive experiments on human SL datasets and these results have proven that our model is consistently superior to baseline methods and predicted SL associations could aid in designing anti-cancer drugs.