AI for Disease Prediction & Early Diagnosis
Welcome to Dr. Jo's Medical AI Research Lab
Latest Research Tools
Deep learning platforms for Alzheimer's disease prediction using genomic data
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.
Latest Publications
Peer-reviewed publications from the last 6 months
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2026
Lee & Jo -DuAL-Net: A Hybrid Deep Learning Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing
Computational and Structural Biotechnology Journal in press
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2025
Jo & Lee -Uncertainty-Aware Genomic Deep Learning Classification of Alzheimer's Disease
Briefings in Bioinformatics
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2025
Lee, Huang, Park et al. -Longitudinal Plasma Proteomics in Alzheimer's Disease
Alzheimer's & Dementia
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2025
Jo, Bice, Nho et al. -LD-Informed Deep Learning for Alzheimer's Disease from Whole-Genome Sequencing
Alzheimer's & Dementia: Translational Research & Clinical Interventions
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.