Taeho Jo

This study integrates schizophrenia-associated SNPs into machine learning models to improve Alzheimer's disease risk prediction, showing modest but significant accuracy gains.

Deep-Block identifies AD-associated genetic loci using WGS data from ADSP, confirming known variants and discovering novel SNPs within top LD blocks.

The paper uses tiled whole genome sequence data to identify genetic signatures associated with Alzheimer's disease using machine learning methods.

The paper introduces SWAT-CNN, a deep learning method for identifying AD-associated SNPs and developing a classification model, achieving an AUC of 0.82.

This paper develops a deep learning framework using tau PET scans to classify Alzheimer's disease and identifies key brain regions associated with the disease.