College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.
Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac080.
In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.
近年来，随着生物信息学和生命科学技术的快速发展，积累了相当数量的生物医学数据，在此基础上，研究人员开发了各种计算方法来发现人类微生物、药物和疾病之间的潜在关联。本文全面概述了从生物数据到计算模型预测微生物、药物和疾病之间潜在相关性的最新进展。首先，我们详细介绍了广泛使用的与识别微生物、药物和疾病之间潜在关系相关的数据集。然后，我们将一系列大量有代表性的计算模型分为网络、矩阵分解、矩阵完成、正则化和人工神经网络五个主要类别进行深入讨论和比较。最后，我们分析了该研究领域的可能挑战和机遇，同时也为进一步提高预测性能提出了一些建议。
