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Open AccessArticle

PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction

1
Department of Physics, College of Science, Huazhong Agricultural University, Wuhan 430070, China
2
School of Basic Medical Science, Hubei University of science and technology, Xianning 437100, China
3
School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Genes 2020, 11(6), 662; https://doi.org/10.3390/genes11060662
Received: 3 May 2020 / Revised: 13 June 2020 / Accepted: 15 June 2020 / Published: 18 June 2020
(This article belongs to the Section Plant Genetics and Genomics)
Rice microRNAs (miRNAs) are important post-transcriptional regulation factors and play vital roles in many biological processes, such as growth, development, and stress resistance. Identification of these molecules is the basis of dissecting their regulatory functions. Various machine learning techniques have been developed to identify precursor miRNAs (pre-miRNAs). However, no tool is implemented specifically for rice pre-miRNAs. This study aims at improving prediction performance of rice pre-miRNAs by constructing novel features with high discriminatory power and developing a training model with species-specific data. PlantMirP-rice, a stand-alone random forest-based miRNA prediction tool, achieves a promising accuracy of 93.48% based on independent (unseen) rice data. Comparisons with other competitive pre-miRNA prediction methods demonstrate that plantMirP-rice performs better than existing tools for rice and other plant pre-miRNA classification. View Full-Text
Keywords: rice; microRNA; prediction; random forest; knowledge-based energy feature rice; microRNA; prediction; random forest; knowledge-based energy feature
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Zhang, H.; Wang, H.; Yao, Y.; Yi, M. PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction. Genes 2020, 11, 662.

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