IEEE J Biomed Health Inform. 2019 May;23(3):1336-1345. doi: 10.1109/JBHI.2018.2852274. Epub 2018 Jul 2.
Combinatorial therapy may reduce drug side effects and improve drug efficacy, making combination therapy a promising strategy to treat complex diseases. However, in the existing computational methods, the natural properties and network knowledge of drugs have not been adequately and simultaneously considered, making it difficult to identify effective drug combinations. Computational methods that incorporate multiple sources of information (biological, chemical, pharmacological, and network knowledge) offer more opportunities to screen synergistic drug combinations. Therefore, we developed a novel Ensemble Prediction framework of Synergistic Drug Combinations (EPSDC) to accurately and efficiently predict drug combinations by integrating information from multiple-sources. EPSDC constructs feature vector of drug pair by concatenating different types of drug similarities, and then uses these groups in a feature-based base predictor. Next, transductive learning is applied on heterogeneous drug-target networks to achieve a network-based score for the drug pair. Finally, two types of ensemble rules are introduced to combine the feature-based score and the network-based score, and then potential drug combinations are prioritized. To demonstrate the effect of the ensemble rule, comprehensive experiments were conducted to compare single models and ensemble models. The experimental results indicated that our method outperformed the state-of-the-art method in five-fold cross validation and de novo prediction tests on the two benchmark datasets. We further analyzed the effect of maximum length of the meta-path and the impacts of different types of features. Moreover, the practical usefulness of our method was confirmed in the predicted novel drug combinations. The source code of EPSDC is available at https://github.com/KDDing/EPSDC.
联合治疗可能会减少药物副作用并提高药物疗效，使联合治疗成为治疗复杂疾病的一种有前途的策略。然而，在现有的计算方法中，药物的自然特性和网络知识没有得到充分和同时的考虑，因此很难识别有效的药物组合。整合多种信息（生物学、化学、药理学和网络知识）的计算方法为筛选协同药物组合提供了更多机会。因此，我们开发了一种新的协同药物组合预测框架（EPSDC），通过整合来自多个来源的信息，准确有效地预测药物组合。EPSDC 通过连接不同类型的药物相似性来构建药物对的特征向量，然后在基于特征的基础预测器中使用这些组。接下来，在异构药物-靶标网络上应用转导学习，为药物对获得基于网络的评分。最后，引入两种类型的集成规则来组合基于特征的评分和基于网络的评分，然后对潜在的药物组合进行优先级排序。为了证明集成规则的效果，我们在两个基准数据集上进行了综合实验，比较了单模型和集成模型。实验结果表明，在 5 倍交叉验证和从头预测测试中，我们的方法在两个基准数据集上均优于最先进的方法。我们进一步分析了最大元路径长度的影响和不同类型特征的影响。此外，还通过预测新的药物组合证实了我们方法的实际用途。EPSDC 的源代码可在 https://github.com/KDDing/EPSDC 上获得。
