EVlncRNA-net: A dual-channel deep learning approach for accurate prediction of experimentally validated lncRNAs

Long non-coding RNAs (lncRNAs) play key roles in numerous biological processes and are associated with various human diseases. High-throughput RNA sequencing (HTlncRNAs) has identified tens of thousands of lncRNAs across species, but only a small fraction have been functionally characterized. While the experimental validation of lncRNAs (EVlncRNAs) using low-throughput methods is increasing, the expensive costs limit the validation to a small subset of HTlncRNAs. Therefore, developing predictive tools to prioritize potentially functional lncRNAs for low-throughput validation is crucial. To address this need, we proposed EVlncRNA-net, a novel deep learning framework based on sequence language processing. This framework incorporates two representation learning modules: EVlncRNA-net (GCN) and EVlncRNA-net (CNN). EVlncRNA-net (GCN) introduces a novel graph construction method and a specialized node encoding technique. This module transforms lncRNA sequences into graphical formats and processes them using graph convolution. EVlncRNA-net (CNN) extracts features from one-hot encoded sequences via convolutional neural networks. Both modules ensure robust feature representation of lncRNA sequences. Tailored for humans, mice, and plants, EVlncRNA-net achieves prediction accuracies of 85.8 %, 83.1 %, and 85.4 %, respectively, outperforming existing methods. The platform is available at https://github.com/rice1ee/EVlncRNA_net/tree/master, serving as a valuable tool for prioritizing lncRNAs for experimental validation.