Dr. Jo and colleagues published a research paper titled "Deep Learning-based Identification of Genetic Variants: Application to Alzheimer's Disease Classification" in Briefings in Bioinformatics in 2022 (doi: 10.1093/bib/bbac022). The study introduced a novel deep learning-based approach to identify genetic variants associated with Alzheimer's disease, demonstrating effective classification performance through analysis of high-dimensional genomic data. [LINK]
"Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout" Authors: Taeho Jo*, Eun Hye Lee This study presents TrUE-Net, an innovative deep learning framework that addresses a critical challenge in genomic-based Alzheimer's disease prediction: the lack of confidence estimates in model predictions. By combining transforme...
"AlphaGenome MCP: A Model Context Protocol Server for Conversational Genomics" We introduce AlphaGenome MCP, a Model Context Protocol server that provides natural language access to AlphaGenome's gene expression prediction capabilities through Claude Desktop and Claude Code. This tool eliminates the need for coding expertise, enabling researchers to perform complex genomic analyses, including ...
"Longitudinal plasma proteomics: relation to incident Alzheimer disease dementia and biomarkers" First author: Dr. Eun Hye Lee (Postdoctoral Fellow, Dr. Jo's Medical AI Lab) Authors: Eun Hye Lee, Yen-Ning Huang, Tamina Park, Shiwei Liu, Nicholas Adzibolosu, Soumilee Chaudhuri, Paula J. Bice, Jeffrey L. Dage, Jared R. Brosch, Sujuan Gao, Liana G. Apostolova, Donna M. Wilcock, Shannon L. Risache...
"Linkage Disequilibrium-Informed Deep Learning Framework for Alzheimer's Disease" This study introduces Deep-Block, a novel multi-stage deep learning framework that incorporates biological knowledge for analyzing large-scale genomic data in Alzheimer's disease. Applied to the Alzheimer's Disease Sequencing Project (ADSP) dataset of 7,416 participants, the framework successfully identif...
Dr. Jo and colleagues published a research paper titled "Circular-SWAT for Deep Learning Based Diagnostic Classification of Alzheimer's Disease: Application to Metabolome Data" in eBioMedicine (doi: 10.1016/j.ebiom.2023.104820) in October 2023. The study introduced a novel Circular-Sliding Window Association Test (c-SWAT) methodology to improve classification accuracy in predicting Alzheimer&#...
Dr. Jo and colleagues published a research paper titled "Deep Learning-based Identification of Genetic Variants: Application to Alzheimer's Disease Classification" in Briefings in Bioinformatics in 2022 (doi: 10.1093/bib/bbac022). The study introduced a novel deep learning-based approach to identify genetic variants associated with Alzheimer's disease, demonstrating effective classificatio...
[2021/04/05] Research Publication: Advances and Challenges in AlphaFold2
Dr. Jo published a comprehensive review article titled "Advances and Challenges in AlphaFold2" in Physics and High Technology in 2021. The article provided an in-depth analysis of the developments and current limitations in AlphaFold2's protein structure prediction capabilities.
Dr. Jo, along with colleagues, published a research paper titled "Deep Learning Detection of Informative Features in tau PET for Alzheimer's Disease Classification" in BMC Bioinformatics (doi: 10.1186/s12859-020-03848-0) in 2020. The study developed a deep learning-based framework combining 3D CNN and LRP algorithms to identify informative features in tau PET images for Alzheimer's disease...
Dr. Jo, along with colleagues, published a systematic review paper titled "Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data" in Frontiers in Aging Neuroscience (doi: 10.3389/fnagi.2019.00220) on August 20, 2019. This comprehensive review examined the application of deep learning approaches in neuroimaging data for improving Alzhe...
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