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Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network

Authors :
Weihan Zhang
Ping Zhang
Weicheng Sun
Jinsheng Xu
Liao Liao
Yunpeng Cao
Yuepeng Han
Source :
PeerJ, Vol 12, p e17396 (2024)
Publication Year :
2024
Publisher :
PeerJ Inc., 2024.

Abstract

Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants.

Details

Language :
English
ISSN :
21678359
Volume :
12
Database :
Directory of Open Access Journals
Journal :
PeerJ
Publication Type :
Academic Journal
Accession number :
edsdoj.99fdf30926c74dc7be017f1f5c975f18
Document Type :
article
Full Text :
https://doi.org/10.7717/peerj.17396