Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0J9, Canada.
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
Bioinformatics. 2020 Aug 15;36(16):4483-4489. doi: 10.1093/bioinformatics/btaa287.
Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs.
We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under precision-recall curve (PR AUC) was 0.58 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models, and found that the DTF outperformed the logistic regression model and achieved similar performance as DeepSynergy using several performance metrics for classification task. Applying the DTF model to predict missing entries in our drug-cell-line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be a valuable in silico tool for prioritizing novel synergistic drug combinations.
Source code and data are available at https://github.com/ZexuanSun/DTF-Drug-Synergy.
Supplementary data are available at Bioinformatics online.
联合疗法已广泛用于癌症治疗。然而，由于可能的药物组合数量巨大，通过实验筛选协同药物对既费时又费钱。因此，计算方法已成为预测和优先考虑协同药物对的重要手段。
我们提出了一种深度张量分解（DTF）模型，它集成了张量分解方法和深度神经网络（DNN），以预测药物协同作用。前者从药物协同信息中提取潜在特征，后者构建二进制分类器来预测药物协同状态。与基于张量的方法相比，DTF 模型在预测药物协同作用方面表现更好。DTF 的精度-召回曲线下面积（PR AUC）为 0.58，而张量方法为 0.24。我们还将 DTF 模型与 DeepSynergy 和逻辑回归模型进行了比较，发现 DTF 优于逻辑回归模型，并在使用几种分类任务性能指标时，其性能与 DeepSynergy 相当。将 DTF 模型应用于预测我们的药物-细胞系张量中的缺失条目，我们从 5 种癌症类型的 10 种细胞系中鉴定出了新的协同药物组合。文献调查显示，其中一些预测的药物协同作用已在体内或体外得到证实。因此，DTF 模型可以成为一种有价值的计算工具，用于优先考虑新的协同药物组合。
源代码和数据可在 https://github.com/ZexuanSun/DTF-Drug-Synergy 上获得。
补充数据可在生物信息学在线获得。
