Guan Zheng-Xing, Li Shi-Hao, Zhang Zi-Mei, Zhang Dan, Yang Hui, Ding Hui
Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China.
Curr Genomics. 2020 Jan;21(1):11-25. doi: 10.2174/1389202921666200214125102.
MicroRNAs, a group of short non-coding RNA molecules, could regulate gene expression. Many diseases are associated with abnormal expression of miRNAs. Therefore, accurate identification of miRNA precursors is necessary. In the past 10 years, experimental methods, comparative genomics methods, and artificial intelligence methods have been used to identify pre-miRNAs. However, experimental methods and comparative genomics methods have their disadvantages, such as time-consuming. In contrast, machine learning-based method is a better choice. Therefore, the review summarizes the current advances in pre-miRNA recognition based on computational methods, including the construction of benchmark datasets, feature extraction methods, prediction algorithms, and the results of the models. And we also provide valid information about the predictors currently available. Finally, we give the future perspectives on the identification of pre-miRNAs. The review provides scholars with a whole background of pre-miRNA identification by using machine learning methods, which can help researchers have a clear understanding of progress of the research in this field.
微小RNA是一类短的非编码RNA分子，能够调控基因表达。许多疾病都与微小RNA的异常表达有关。因此，准确识别微小RNA前体是必要的。在过去十年中，实验方法、比较基因组学方法和人工智能方法都被用于识别前体微小RNA。然而，实验方法和比较基因组学方法都有其缺点，比如耗时。相比之下，基于机器学习的方法是更好的选择。因此，本综述总结了基于计算方法的前体微小RNA识别的当前进展，包括基准数据集的构建、特征提取方法、预测算法以及模型的结果。并且我们还提供了有关当前可用预测器的有效信息。最后，我们给出了前体微小RNA识别的未来展望。本综述为学者们提供了使用机器学习方法进行前体微小RNA识别的完整背景，这有助于研究人员清楚地了解该领域的研究进展。
