Advancements in Gene Structure Prediction: Innovation and Prospects of Deep Learning Models Apply in Multi-species

Abstract

In recent years, advancements in gene structure prediction have been significantly driven by the integration of deep learning technologies into bioinformatics. Transitioning from traditional thermodynamics and comparative genomics methods to modern deep learning-based models such as CDSBERT, DNABERT, RNA-FM, and PlantRNA-FM prediction accuracy and generalization have seen remarkable improvements. These models, leveraging genome sequence data along with secondary and tertiary structure information, have facilitated diverse applications in studying gene functions across animals, plants, and humans. They also hold substantial potential for multi-application in early disease diagnosis, personalized treatment, and genomic evolution research. This review combines traditional gene structure prediction methods with advancements in deep learning, showcasing applications in functional region annotation, protein-RNA interactions, and cross-species genome analysis. It highlights their contributions to animal, plant, and human disease research while exploring future opportunities in cancer mutation prediction, RNA vaccine design, and CRISPR gene editing optimization. The review also emphasizes future directions, such as model refinement, multimodal integration, and global collaboration. By offering a concise overview and forward-looking insights, this article aims to provide a foundational resource and practical guidance for advancing nucleic acid structure prediction research.