合成致死（SL）对是同时丧失功能导致细胞死亡的基因对，而单独任一基因的破坏性突变不会影响细胞的存活。这使得 SL 对成为精准癌症治疗的有吸引力的靶点，因为靶向 SL 对的未受损基因可以选择性地杀死已经含有受损基因的癌细胞。由于寻找真正的 SL 对的难度，特别是在特定细胞类型上，当前的计算方法只能提供有限的见解，因为忽略了细胞上下文依赖性和 SL 对的机制理解的关键方面。因此，SL 目标的识别仍然依赖于昂贵且耗时的实验方法。在这项工作中，我们将细胞系特异性多组学数据应用于专门设计的深度学习模型来预测细胞系特异性 SL 对。通过将多种类型的细胞特异性组学数据与自我关注模块结合起来，我们将基因关系表示为图表。我们的方法以细胞特异性的方式实现了 SL 对的预测，并展示了促进发现癌症治疗的细胞特异性 SL 靶点的潜力，为揭示癌症生物学中 SL 起源的机制提供了工具。我们方法的代码和数据可以在 https://github.com/promethiume/SLwise 找到。© 2023 作者。

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell's survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise.© 2023 The Authors.