Raelynn Chen

G2DBridge links genetic variation to disease risk by modeling imaging-derived phenotypes as intermediate traits, improving Alzheimer's Disease prediction over polygenic risk scores without new data.

This paper introduces DEEN, a deep learning method that improves polygenic risk score prediction by modeling both linear and non-linear SNP effects, showing better performance on UK Biobank and All of Us datasets.

The paper introduces MIXER, a framework that combines multiple metrics for feature selection and predictive modeling, outperforming individual criteria in Alzheimer's disease analysis.