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Updated: Oct 23, 2025
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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
Published on: January 21, 2020
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PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions
Haoran Zhou1, Jael Sanyanda Wekesa1, Yushi Luan2
1School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
BMC Bioinformatics
|August 25, 2021
Summary
This study introduces PRPI-SC, a novel deep learning model that accurately predicts interactions between plant long non-coding RNAs (lncRNAs) and RNA-binding proteins (RBPs) using sequence and structural data.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- Plant long non-coding RNAs (lncRNAs) are crucial regulators in biological processes.
- Understanding lncRNA function necessitates identifying their interacting RNA-binding proteins (RBPs).
- Predicting lncRNA-RBP interactions remains a significant computational challenge.
Purpose of the Study:
- To develop a robust computational model for predicting plant lncRNA-protein interactions.
- To leverage sequence and structural information for enhanced prediction accuracy.
- To provide a tool for elucidating lncRNA functions in plants.
Main Methods:
- An ensemble deep learning model, PRPI-SC, was developed.
- The model utilizes stacked denoising autoencoder and convolutional neural network architectures.
- Predictions are based on k-mer features derived from RNA and protein sequences.
Main Results:
- PRPI-SC achieved high prediction accuracy on Arabidopsis thaliana (88.9%) and Zea mays (82.6%) datasets.
- The model demonstrated strong performance on independent plant RNA-protein interaction datasets.
- PRPI-SC shows good generalization capabilities for non-plant data.
Conclusions:
- PRPI-SC accurately predicts plant lncRNA-protein interactions, aiding in functional and expression studies.
- The model's strong generalization ability extends its utility beyond plant species.
- PRPI-SC offers a valuable tool for advancing research in lncRNA biology.
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