De novo miRNA-lncRNA Interactions Prediction via K-Square Encoding and Mixed Network

Non-coding RNAs play a crucisal role in regulating various biological activities such as genetics and metabolism in plants. Traditional biological methods suffer from long research cycles and high costs. In recent years, bioinformatics methods combining deep learning have mainly focused on modifying network structures, with limited progress in extracting and describing features of RNA sequences and structures. In this study, we propose a novel two-dimensional Kmer cross-encoding approach based on an improved traditional Kmer encoding to predict miRNA-lncRNA interactions. This encoding integrates the features of miRNA and lncRNA into a meaningful encoded image, allowing for interactive interpretation. Furthermore, it combines neural networks that process sequence and image information. The proposed method named PmliGKKS was trained and tested on species from four different species, with independent testing conducted on two additional species. The results obtained using our approach demonstrate significant improvements compared to several state-of-the-art methods.