DeepLMI: Deep Feature Mining with a Globally Enhanced Graph Convolutional Network for Robust lncRNA–miRNA Interaction Prediction

Abstract Motivation Interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play pivotal roles in gene regulation and disease progression, notably through mechanisms such as competitive miRNA sponging. Accurate identification of lncRNA–miRNA interactions is therefore essential for understanding disease mechanisms and discovering therapeutic targets. However, current knowledge is largely derived from labor-intensive and costly biological experiments, underscoring the need for reliable computational approaches. Results We propose DeepLMI, a novel deep learning framework for lncRNA–miRNA interaction prediction that integrates deep feature mining with a globally enhanced graph convolutional network. To effectively capture the distinct properties of lncRNAs and miRNAs, DeepLMI employs specialized feature extraction modules: for lncRNAs, we combine sequence pre-training with self-attention mechanisms to learn multi-scale semantic representations; for miRNAs, we fuse heterogeneous features through a graph convolutional encoder. To further address the sparsity and structural complexity of known RNA interaction networks, we design a Global-Enhanced Graph Convolutional Network (GE-GCN) that jointly models local neighborhood information and global topological signals. The embeddings learned for lncRNAs and miRNAs are then integrated to infer interaction probabilities. Extensive experiments across multiple datasets and evaluation settings demonstrate that DeepLMI consistently outperforms existing state-of-the-art methods and exhibits strong robustness, highlighting its potential as a valuable tool for RNA interaction analysis and disease research. Availability The codes and data are publicly available at https://github.com/Hhhzj-7/DeepLMI. Supplementary information Supplementary data are available at Bioinformatics online.