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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Predicting drug synergy for precision medicine using network biology and machine learning
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Predicting drug synergy for precision medicine using network biology and machine learning

机译：采用网络生物学和机器学习预测精密药物的药物协同作用

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Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.
机译：鉴定患者的有效药物组合是一种昂贵且耗时的程序，特别是对于体外实验。为了加速协同药物发现过程，我们提出了一种新的分类模型，以鉴定使用Silico网络生物学方法的更有效的抗癌药物对。基于假设药物协同作用来自生物网络的集体影响，因此，我们开发了六种网络生物学特征，包括通过使用个体药物扰动的转录组简档和相关的药物扰动网络的重叠和距离。生物网络分析。使用公开的药物协同数据库和三种机器学习（ML）方法，培训模型以区分阳性（协同）和负（非营养）药物组合。在测试病例中评估了所提出的模型，以预测最有前途的网络生物学特征，即网络度活动，即药物对之间的协同效应主要由来自两种药物的互补信号传导途径或分子网络占据。

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