Variant Effect Prediction

Variant effect prediction (VEP) aims to computationally determine the impact of genetic variations on protein function and disease risk. Current research focuses on improving VEP accuracy using advanced machine learning models, including protein and genomic language models, often incorporating Siamese networks and Gaussian process regression, and leveraging experimental data like Deep Mutational Scanning. A key trend is the development of disease-specific models, enhancing prediction accuracy and clinical relevance compared to general-purpose models. Improved VEP methods hold significant potential for accelerating disease diagnosis, drug discovery, and precision medicine initiatives.