College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
Math Biosci Eng. 2022 Apr 6;19(6):5754-5771. doi: 10.3934/mbe.2022269.
The study of drug side effects is a significant task in drug discovery. Candidate drugs with unaccepted side effects must be eliminated to prevent risks for both patients and pharmaceutical companies. Thus, all side effects for any candidate drug should be determined. However, this task, which is carried out through traditional experiments, is time-consuming and expensive. Building computational methods has been increasingly used for the identification of drug side effects. In the present study, a new path-based method was proposed to determine drug side effects. A heterogeneous network was built to perform such method, which defined drugs and side effects as nodes. For any drug and side effect, the proposed path-based method determined all paths with limited length that connects them and further evaluated the association between them based on these paths. The strong association indicates that the drug has a side effect with a high probability. By using two types of jackknife test, the method yielded good performance and was superior to some other network-based methods. Furthermore, the effects of one parameter in the method and heterogeneous network was analyzed.
药物副作用研究是药物发现中的一项重要任务。具有不可接受副作用的候选药物必须被淘汰，以防止患者和制药公司面临风险。因此，应该确定任何候选药物的所有副作用。然而，这项通过传统实验进行的任务既耗时又昂贵。因此，越来越多的人开始使用计算方法来识别药物副作用。在本研究中，提出了一种新的基于路径的方法来确定药物副作用。构建了一个异构网络来执行这种方法，其中将药物和副作用定义为节点。对于任何药物和副作用，所提出的基于路径的方法都会确定连接它们的有限长度的所有路径，并进一步根据这些路径评估它们之间的关联。强关联表明该药物极有可能具有副作用。通过使用两种类型的 jackknife 测试，该方法表现良好，优于其他一些基于网络的方法。此外，还分析了该方法和异构网络中一个参数的影响。
