Go Hun Seo

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.

This study identifies PKHD1L1 as a cause of autosomal recessive nonsyndromic hearing loss through exome sequencing and functional analysis in human families and animal models.

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

The paper evaluates a quality threshold for whole exome sequencing variants to determine when Sanger sequencing confirmation is unnecessary, finding high accuracy with specific quality score and allele fraction thresholds.

This study evaluates an automated variant interpretation system for whole exome sequencing, finding it improves diagnostic yield for genetic disorders by 51%.

This paper introduces a Bayesian framework method called BayesianOR to improve the interpretation of genetic variants for PS4 criteria in rare diseases using in-house frequency data.