Song Tao, Wang Gan, Ding Mao, Rodriguez-Paton Alfonso, Wang Xun, Wang Shudong
College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China.
Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo Boadilla del Monte, 28660, Madrid, Spain.
Mol Inform. 2022 May;41(5):e2100200. doi: 10.1002/minf.202100200. Epub 2021 Dec 30.
With deep learning creeping up into the ranks of big data, new models based on deep learning and massive data have made great leaps forward rapidly in the field of drug repositioning. However, there is no relevant review to summarize the transformations and development process of models and their data in the field of drug repositioning. Among all the computational methods, network-based methods play an extraordinary role. In view of these circumstances, understanding and comparing existing network-based computational methods applied in drug repositioning will help us recognize the cutting-edge technologies and offer valuable information for relevant researchers. Therefore, in this review, we present an interpretation of the series of important network-based methods applied in drug repositioning, together with their comparisons and development process.
随着深度学习在大数据领域的逐渐兴起，基于深度学习和海量数据的新模型在药物重新定位领域迅速取得了巨大进展。然而，目前尚无相关综述来总结药物重新定位领域中模型及其数据的变革与发展历程。在所有计算方法中，基于网络的方法发挥着非凡的作用。鉴于此，了解和比较药物重新定位中现有的基于网络的计算方法将有助于我们认识前沿技术，并为相关研究人员提供有价值的信息。因此，在本综述中，我们对药物重新定位中应用的一系列重要的基于网络的方法进行了解读，并对它们进行了比较以及介绍了其发展历程。
