Kyoungyeul Lee

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

This paper introduces a deep learning model using multiple instance learning and learning to rank to prioritize variants in rare Mendelian disease genomic testing, achieving high accuracy in identifying disease-causing variants.

The paper develops a system that integrates variant quality control into prioritization to improve the accuracy of identifying disease-causing variants in rare disorders.

3Cnet predicts pathogenic variants using deep recurrent neural networks and evolutionary conservation, achieving higher recall than existing tools.