# Machine learning for plant microRNA prediction: A systematic review

**Authors:** Shyaman Jayasundara, Sandali Lokuge, Puwasuru Ihalagedara, Damayanthi Herath  
**arXiv:** [2106.15159](https://arxiv.org/abs/2106.15159)  
**Date:** 2021-06-29  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2106.15159v1/

## Key points

- MicroRNAs (miRNAs) are small non-coding RNAs that regulate genes in biology
- Experimental methods for determining miRNA sequence and structure are expensive and time-consuming
- Computational and machine learning-based approaches have been used to predict novel miRNAs
- Numerous studies have focused on identifying miRNAs in plants using data science and machine learning
- The review examines different approaches, learning algorithms, features, datasets, and evaluation criteria used in past research efforts
- The aim is to help researchers understand previous studies and find new ways to address limitations encountered
- Plant-specific computational methods for miRNA identification are needed for advancements in miRNA research in plants
- Refined understanding can lead to more accurate and efficient techniques tailored to plant species.

## Summary

In the field of biology, microRNAs (miRNAs) are small non-coding RNAs that play a crucial role in post-transcriptional gene regulation. However, determining the sequence and structure of miRNAs through experimental methods is both expensive and time-consuming. To overcome these limitations, researchers have turned to computational and machine learning-based approaches for predicting novel miRNAs. With the integration of data science and machine learning in biology, numerous studies have been conducted to identify miRNAs using different computational methods and miRNA features. This systematic review focuses specifically on machine learning methods developed for identifying miRNAs in plants. By examining various approaches, including the learning algorithms employed, features considered, datasets used, and evaluation criteria applied, this review provides a comprehensive overview of past research efforts. The aim is to help researchers gain a detailed understanding of previous studies and identify new avenues for addressing the limitations encountered in those studies. The findings of this review emphasize the need for plant-specific computational methods for miRNA identification which can contribute to advancements in miRNA research in plants and pave the way for improved post-transcriptional gene regulation studies. Furthermore, this review highlights how such refined understanding can enable researchers to develop more accurate and efficient techniques tailored specifically to plant species.