论文标题
通过合奏学习预测药物协同作用
Prediction of Drug Synergy by Ensemble Learning
论文作者
论文摘要
治疗复杂疾病（例如癌症）的有前途的方法之一是联合治疗。由于组合复杂性，机器学习模型在该领域可能很有用，在该领域，最近在确定协同组合方面已实现了重大改进。在这项研究中，我们研究了不同化合物表示在预测药物协同作用方面的有效性。在大型药物组合屏幕数据集上，我们首先证明了以前尚未用于此问题的有希望的表示形式，然后我们在表示模型组合上提出了一个胜过每个基线模型的合奏。
One of the promising methods for the treatment of complex diseases such as cancer is combinational therapy. Due to the combinatorial complexity, machine learning models can be useful in this field, where significant improvements have recently been achieved in determination of synergistic combinations. In this study, we investigate the effectiveness of different compound representations in predicting the drug synergy. On a large drug combination screen dataset, we first demonstrate the use of a promising representation that has not been used for this problem before, then we propose an ensemble on representation-model combinations that outperform each of the baseline models.
