Traditional drug experiments to find synergistic drug pairs are time-consuming and expensive due to the numerous possible combinations of drugs that have to be examined. Thus, computational methods that can give suggestions for synergistic drug investigations are of great interest. Here, we propose a Non-negative Matrix Tri-Factorization (NMTF) based approach that leverages the integration of different data types for predicting synergistic drug pairs in multiple specific cell lines. Our computational framework relies on a network-based representation of available data about drug synergism, which also allows integrating genomic information about cell lines. We computationally evaluate the performances of our method in finding missing relationships between synergistic drug pairs and cell lines, and in computing synergy scores between drug pairs in a specific cell line, as well as we estimate the benefit of adding cell line genomic data to the network. Our approach obtains very good performance (Average Precision Score equal to 0.937, Pearson's correlation coefficient equal to 0.760) when cell line genomic data and rich data about synergistic drugs in a cell line are considered. Finally, we systematically searched our top-scored predictions in the available literature and in the NCI ALMANAC, a well-known database of drug combination experiments, proving the goodness of our findings.

中文翻译：

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通过非负矩阵三因子分解预测药物协同作用


由于必须检查多种可能的药物组合，寻找协同药物对的传统药物实验既耗时又昂贵。因此，能够为协同药物研究提供建议的计算方法引起了人们的极大兴趣。在这里，我们提出了一种基于非负矩阵三因子分解（NMTF）的方法，该方法利用不同数据类型的集成来预测多个特定细胞系中的协同药物对。我们的计算框架依赖于基于网络的药物协同作用可用数据的表示，这也允许整合有关细胞系的基因组信息。我们通过计算评估我们的方法在寻找协同药物对和细胞系之间缺失的关系以及计算特定细胞系中药物对之间的协同得分方面的性能，以及我们估计将细胞系基因组数据添加到网络的好处。当考虑细胞系基因组数据和细胞系中协同药物的丰富数据时，我们的方法获得了非常好的性能（平均精度得分等于0.937，皮尔逊相关系数等于0.760）。最后，我们在现有文献和 NCI 年鉴（著名的药物组合实验数据库）中系统地搜索了得分最高的预测，证明了我们研究结果的有效性。