Researchers from the China National Center for Bioinformation, part of the Chinese Academy of Sciences, have developed a groundbreaking AI framework capable of precisely predicting RNA splicing and isoform usage. Led by Professor Gao Yuan, the team introduced the Hierarchical Explainable LSTM for Isoform eXpression (HELIX) framework. Published in Nature Computational Science on May 19, this study offers significant advancements for understanding splicing regulatory patterns, interpreting pathogenic variants, and advancing precision medicine research. RNA serves as the critical intermediary between DNA and proteins, yet a single gene can produce numerous RNA variants through a process known as alternative splicing. This mechanism allows cells to generate a diverse array of protein isoforms with distinct functions, significantly expanding the complexity of the human transcriptome. Dysregulation of this process, often driven by complex interactions between regulatory elements, RNA-binding proteins, and the tissue microenvironment, is closely linked to major diseases, including cancer. Historically, accurately characterizing and predicting these isoform patterns across different tissues and disease states has been a formidable challenge. HELIX addresses these limitations through a novel two-layer deep-learning architecture. The system integrates genomic sequence features with tissue-specific expression profiles of 1,499 RNA-binding proteins. It then employs long short-term memory networks to effectively capture the complex dependencies and competitive relationships among multiple splice sites. Trained and optimized on large-scale RNA sequencing datasets covering 30 distinct human tissues, the model achieves superior accuracy in quantifying complex transcript structures compared to existing mainstream methods. In disease-related applications, HELIX demonstrated a powerful ability to decipher aberrant splicing. Using large cohorts of colorectal cancer patients, the researchers identified widespread splicing dysregulation and abnormal isoform usage within tumor cells. These alterations showed strong correlations with genomic mutations, RNA-binding protein dysregulation, and patient clinical outcomes. These findings suggest that splicing abnormalities can serve as vital molecular signatures for understanding tumor progression and guiding patient stratification. Furthermore, the team developed a single-cell extension of the framework, known as scHELIX, specifically designed for single-cell RNA sequencing data. This tool enables high-resolution profiling of transcript isoform usage across different cell types and tumor subpopulations. The analysis revealed distinct splicing and isoform usage patterns among tumor subclones, offering new insights into tumor evolution and potential therapeutic target discovery. Overall, the HELIX and scHELIX frameworks constitute a robust AI toolkit for unraveling RNA splicing regulation under complex biological conditions. By deepening the understanding of tissue-specific and disease-related splicing mechanisms, this work provides essential computational tools and theoretical support for cancer subtyping, pathogenic variant annotation, and the future development of precision medicine strategies.