Variant Pathogenicity

Variant pathogenicity research aims to understand how genetic variations affect biological function and disease risk, focusing on accurate prediction of variant effects. Current research emphasizes the development of sophisticated machine learning models, including large language models, graph neural networks, and siamese networks, often incorporating knowledge-driven feature selection and disease-specific fine-tuning to improve prediction accuracy. These advancements hold significant implications for precision medicine, enabling more accurate diagnoses, personalized treatments, and improved understanding of disease mechanisms.