Graph based recurrent network for context specific synthetic lethality prediction
作者机构：Academy of Medical Engineering and Translational Medicine Tianjin University Department of Bioinformatics Institute of Health Service and Transfusion MedicineBeijing School of Medicine Tsinghua University School of Informatics Xiamen University
出 版 物：《Science China Life Sciences》 (中国科学:生命科学(英文版))
年 卷 期：2024年
核心收录：
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
基 金：supported by the National Key Research and Development Program of China (2023YFC2604400) the National Natural Science Foundation of China (62103436)
摘 要：The concept of synthetic lethality (SL) has been successfully used for targeted therapies. To further explore SL for cancer therapy, identifying more SL interactions with therapeutic potential are essential. Recently, graph neural network-based deep learning methods have been proposed for SL prediction, which reduce the SL search space of wet-lab based methods. However, these methods ignore that most SL interactions depend strongly on genetic context, which limits the application of the predicted results. In this study, we proposed a graph recurrent network-based model for specific context-dependent SL prediction (SLGRN). In particular, we introduced a Graph Recurrent Network-based encoder to acquire a context-specific, low-dimensional feature representation for each node, facilitating the prediction of novel SL. SLGRN leveraged gate recurrent unit (GRU) and it incorporated a context-dependent-level state to effectively integrate information from all nodes. As a result, SLGRN outperforms the state-of-the-arts models for SL prediction. We subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival analysis. Through in vitro experiments and retrospective clinical analysis, we emphasize the potential clinical significance of this context-specific SL prediction model.
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