Next Article in Journal
Rapid Direct Nucleic Acid Amplification Test without RNA Extraction for SARS-CoV-2 Using a Portable PCR Thermocycler
Previous Article in Journal
Analysis of HBV Genomes Integrated into the Genomes of Human Hepatoma PLC/PRF/5 Cells by HBV Sequence Capture-Based Next-Generation Sequencing
Open AccessArticle

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

Department of Physics, College of Science, Huazhong Agricultural University, Wuhan 430070, China
School of Basic Medical Science, Hubei University of science and technology, Xianning 437100, China
School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
Author to whom correspondence should be addressed.
Genes 2020, 11(6), 662;
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
Show Figures

Figure 1

MDPI and ACS Style

Zhang, H.; Wang, H.; Yao, Y.; Yi, M. PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction. Genes 2020, 11, 662.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop