Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.
Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.
复制标题
DOI：
10.3389/fgene.2022.1103092
复制
发表时间：
2022
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
合成致死（SL）基因相互作用被认为是研究治疗癌症的潜在靶向疗法的一个有希望的焦点。然而，为了发现潜在的 SL 关系而在湿实验室实验筛选方面投入了昂贵的时间和劳动力，这推动了计算方法的发展。尽管图神经网络（GNN）模型在 SL 基因对的预测中表现良好，但现有的基于 GNN 的模型并不是为预测与体外实验验证更相关的癌细胞特异性 SL 相互作用而设计的。此外，现有方法也没有充分利用生物特征的多种图形表示来提高预测性能。在这项工作中，我们提出了MVGCN-iSL，一种新颖的多视图图卷积网络（GCN）模型，通过结合五个生物图特征和多组学数据来预测癌细胞特异性SL基因对。应用最大池化操作来集成从 GCN 模型获得的五个特定于图的表示。随后，深度神经网络 (DNN) 模型作为预测模块来预测单个癌细胞 (iSL) 中的 SL 相互作用。大量实验验证了该模型成功集成了多个图特征和在预测潜在 SL 基因对方面的最先进性能以及对新基因的泛化能力。
Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model’s successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.
DOI：
10.1038/sj.mt.6300116
发表时间：
2007-05
期刊：
Molecular therapy : the journal of the American Society of Gene Therapy
影响因子：
--
作者：
Grimm D;Kay MA
通讯作者：
Kay MA