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 timeconsuming.
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.