College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China.
Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China.
Brief Bioinform. 2023 Nov 22;25(1). doi: 10.1093/bib/bbad522.
Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.
联合治疗与单药治疗相比显示出了巨大的潜力。然而，由于癌症药物数量的爆炸式增长，协同药物组合的筛选既昂贵又耗时。协同药物组合是指同时使用两种或多种药物来增强治疗效果。目前，已经开发出许多计算方法来预测抗癌药物的协同作用。然而，对于如何在不同的粒度级别挖掘药物和细胞系数据以预测协同抗癌药物组合，还没有足够的探索。因此，本研究提出了一种基于超图Transformer 的粒度级信息融合策略，称为 HypertranSynergy，用于预测抗癌药物的协同作用。HypertranSynergy 使用超图引入癌细胞系和药物组合之间的协同连接。然后，粗粒度信息提取（CIE）模块将超图与Transformer 合并用于节点嵌入。在 CIE 模块中，Contranorm 是一个归一化层，可以减轻过平滑问题，而高斯噪声则解决了局部信息差距问题。此外，细粒度信息提取（FIE）模块通过使用来自药物/细胞系特征的相似性感知矩阵来评估细粒度信息对预测的影响。CIE 和 FIE 模块都集成到了 HypertranSynergy 中。此外，在分类任务的 5 折交叉验证中，HypertranSynergy 实现了 AUC 为 0.93${\pm }$0.01 和 AUPR 为 0.69${\pm }$0.02，在回归任务的 5 折交叉验证中，RMSE 为 13.77${\pm }$0.07 和 PCC 为 0.81${\pm }$0.02。这些结果优于大多数最先进的模型。
Zotero 插件
