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

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 sel

SUBMITTER: Zhang W 

PROVIDER: S-EPMC11122044 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature