Author:
Zhao Jiawen,Guo Zhonglong,Yang Xiaozeng
Publisher
Springer Science and Business Media LLC
Reference10 articles.
1. Axtell, M.J., and Meyers, B.C. (2018). Revisiting criteria for plant MicroRNA annotation in the era of big data. Plant Cell 30, 272–284.
2. Bugnon, L.A., Yones, C., Milone, D.H., and Stegmayer, G. (2021). Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning. Brief Bioinf 22, bbaa184.
3. Kuang, Z., Wang, Y., Li, L., and Yang, X. (2019). miRDeep-P2: accurate and fast analysis of the microRNA transcriptome in plants. Bioinformatics 35, 2521–2522.
4. Kuang, Z., Zhao, Y., and Yang, X. (2023). Machine learning approaches for plant miRNA prediction: challenges, advancements, and future directions. Agr Commun 1, 100014.
5. Li, G., Chen, C., Chen, P., Meyers, B.C., and Xia, R. (2024). sRNAminer: a multifunctional toolkit for next-generation sequencing small RNA data mining in plants. Sci Bull 69, 784–791.