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.

中文翻译：

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使用机器学习方法进行 MicroRNA 前体识别的简要调查

MicroRNAs 是一组短的非编码 RNA 分子，可以调节基因表达。许多疾病都与miRNA的异常表达有关。因此，准确鉴定miRNA前体是必要的。近10年来，实验方法、比较基因组学方法和人工智能方法已被用于鉴定pre-miRNAs。然而，实验方法和比较基因组学方法都有其缺点，例如耗时。相比之下，基于机器学习的方法是更好的选择。因此，综述总结了基于计算方法的pre-miRNA识别的当前进展，包括基准数据集的构建、特征提取方法、预测算法和模型的结果。我们还提供有关当前可用预测器的有效信息。最后，我们给出了对 pre-miRNA 鉴定的未来展望。该综述为学者们提供了利用机器学习方法进行pre-miRNA鉴定的整体背景，有助于研究人员对该领域的研究进展有一个清晰的认识。