Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
Imperial College London.
Brief Bioinform. 2018 Sep 28;19(5):878-892. doi: 10.1093/bib/bbx017.
Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
实验性药物开发既耗时又昂贵，而且仅限于相对较少的目标。然而，最近的研究表明，重新定位现有药物比从头开始进行实验性药物开发更有效，可以最大限度地降低成本和风险。先前的研究已经证明，网络分析是实现这一目标的多功能平台，因为生物网络用于模拟许多不同生物概念之间的相互作用。本研究试图综述基于网络的方法在药物重定位中预测药物靶标。对于每种方法，都描述了首选的数据集类型，并讨论了它们的优点和局限性。对于每种方法，我们都试图提供简要描述，并根据其性能指标进行评估。我们得出的结论是，应该整合不同且互补的数据，因为每种类型的数据集都揭示了有关生物体信息的独特方面。我们还建议在这个快速发展的研究领域中，应用标准的评估指标和数据集是至关重要的。
