Alternative Splicing

Alternative splicing is a process where a single gene can produce multiple mRNA isoforms with potentially different functions, impacting cellular processes and disease development. Current research focuses on improving the accuracy and efficiency of identifying and characterizing these isoforms, employing deep learning models (like attention-based networks and transformers) and novel algorithms (including contrastive learning and Bayesian approaches) to analyze genomic data and predict isoform functions. These advancements are crucial for understanding gene regulation, diagnosing genetic disorders, and developing targeted therapies, as well as for improving the accuracy of forensic analysis in various fields.