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AI for Disease Prediction & Early Diagnosis

Welcome to Dr. Jo's Medical AI Research Lab

Research Tools

AI platforms for Alzheimer's disease genomics and systematic review screening

DuAL-Net

Dual Approach Local-global Network

Hybrid framework combining local and global genomic features for AD prediction from WGS.

TrUE-Net

Transformer Uncertainty Ensemble

Uncertainty-aware genomic deep learning framework using transformer ensembles for AD classification.

SWAT-web

Sliding Window Association Test

Genome-wide sliding window association analysis of whole-genome sequencing data using deep learning.

ReviewFast

AI Screening for Systematic Reviews

Ranks search results against a review's eligibility criteria, then supports screening, stopping, and data extraction.

Latest Publications

Recent publications and preprints

Research Areas

Core research themes of our lab

Genomics & AI

We combine large-scale genomic data with AI to identify genetic variants linked to Alzheimer's disease, helping identify individuals at higher risk more accurately.

Neuroimaging & AI

Combining brain imaging technologies like PET and MRI with AI to detect early changes in the brain associated with Alzheimer's before symptoms appear.

Metabolomics / Proteomics & AI

Analyzing metabolites and proteins in biological fluids to track biochemical changes as Alzheimer's progresses, predicting progression before cognitive decline begins.

Precision Medicine

Integrating genomics, neuroimaging, and metabolomics with AI to develop precise predictions of disease progression and personalized treatment strategies.

Research Support

NIH National Institute on Aging Alzheimer's Association ADNI AIAD ADSP Anthropic