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JoLab.AI :: Research

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Genomics & AI

We combine large-scale genomic data with artificial intelligence (AI) to identify genetic variants linked to Alzheimer’s disease. By using machine learning techniques, we can analyze vast datasets to find key genetic markers, helping us identify individuals at higher risk more accurately.

Selected Publications

  • LD-informed deep learning for Alzheimer's gene loci detection using WGS data (Taeho Jo et al., 2023) LD-aware deep learning approach for AD gene loci discovery. [LINK]
  • Deep learning–based genome-wide association analysis in Alzheimer’s disease (Presenting Author: Taeho Jo, AAIC 2021) Used a CNN on 12+ million SNPs for AD classification. [LINK]
  • Deep learning-based identification of genetic variants (Taeho Jo et al., Briefings in Bioinformatics, 2022) SWAT-CNN approach, achieving AUC of 0.82. [LINK]
  • Deep Learning-based SWAT-Tab Approach for Identifying Genetic Variants using Whole Genome Sequencing (Presenting Author: Taeho Jo, AAIC 2023) SWAT-TAB method on ADSP WGS data with improved efficiency. [LINK]
 

Neuroimaging & AI

Combining brain imaging technologies like PET and MRI with AI allows us to detect early changes in the brain associated with Alzheimer’s. This approach helps identify signs of the disease before symptoms appear, enabling timely interventions.

Selected Publications

  • Deep Learning-based Integration of Neuroimaging and Genetic Data for Classification of Alzheimer’s Disease (Presenting Author: Taeho Jo, AAIC 2023) 90.8% accuracy analyzing tau PET & APOE loci. [LINK]
  • Deep learning detection of informative features in tau PET for Alzheimer’s disease classification (Taeho Jo et al., BMC Bioinformatics, 2020) 3D CNN, 90.8% accuracy on tau PET scans. [LINK]
  • Deep learning detection of informative features in [18F] flortaucipir PET (Presenting Author: Taeho Jo, AAIC 2020) 3D CNN + LRP for classification & visualization. [LINK]
  • Deep Learning in Alzheimer’s Disease: Diagnostic Classification and Prognostic Prediction (Taeho Jo et al., Frontiers in Aging Neuroscience, 2019) Systematic review of deep learning with multimodal neuroimaging. [LINK]
  • Multimodal-3DCNN: Diagnostic Classification of Alzheimer’s Disease (Presenting Author: Taeho Jo, AAIC 2019) 3D CNN + PET/MRI + APOE & demographic info with ~329 samples. [LINK]
  • Multimodal-CNN: Improved Accuracy of MRI-based Classification of Alzheimer’s Disease (Presenting Author: Taeho Jo, AAIC 2018) Gram matrix approach for combining clinical data & imaging. [LINK]
 

Metabolomics & AI

By analyzing metabolites in blood and other fluids, we can track biochemical changes as Alzheimer’s progresses. Using AI, we can predict the disease’s progression and customize treatment strategies even before cognitive decline begins.

Selected Publications

  • Circular-SWAT for deep learning based diagnostic classification of Alzheimer’s disease (Taeho Jo et al., eBioMedicine, 2023) Serum-based metabolomics, accuracy up to 80.8%. [LINK]
  • Novel circling SWAT for deep learning classification of Alzheimer’s disease (Presenting Author: Taeho Jo, AAIC 2022) 781 lipids from ADNI (CN, MCI, AD); Circling SWAT approach. [LINK]
 

Precision Medicine

By integrating genomics, neuroimaging, and metabolomics with AI, we can develop more precise predictions of disease progression. This personalized approach tailors treatments to individual patients, improving early detection and care.

Selected Publications

  • Deep Learning-based Integration of Neuroimaging and Genetic Data (Presenting Author: Taeho Jo, AAIC 2023) Multi-modal approach yields 90.8% AD classification accuracy. [LINK]
  • Deep learning-based identification of genetic variants (Taeho Jo et al., Briefings in Bioinformatics, 2022) Highlighting APOE region; synergy with imaging/metabolomics. [LINK]
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