LD‐informed deep learning for Alzheimer's gene loci detection using WGS data

Taeho Jo, Paula Bice, Kwangsik Nho, Andrew J. Saykin, the Alzheimer's Disease Sequencing Project, Alzheimer & Dementia TRCI (2025) Deep‐Block is a multi‐stage deep learning framework designed ...

Circular-SWAT for deep learning based diagnostic classification of Alzheimer’s disease: Application to metabolome data

Taeho Jo, Junpyo Kima, Paula Bice, Kevin Huynh, Tingting Wang, Matthias Arnold, Peter J. Meikle, Corey Giles, Rima Kaddurah-Daoukf, Andrew J. Saykina, Kwangsik Nho, eBioMedicine (2023) This study intr...

Deep Learning-based Integration of Neuroimaging and Genetic Data for Classification of Alzheimer's Disease

Taeho Jo, Kwangsik Nho, Shannon L. Risacher, Andrew J. Saykin, AAIC (2023) This study introduces a new deep learning method using CNNs to analyze tau PET images and identify Alzheimer's Disease (A...

Deep Learning-based SWAT-Tab Approach for Identifying Genetic Variants using Whole Genome Sequencing

Taeho Jo, Kwangsik Nho, Andrew J. Saykin, AAIC (2023) The study introduces SWAT-TAB, an evolved form of SWAT-CNN, optimized for identifying genetic variants in Alzheimer's disease (AD). It utilize...

Novel circling SWAT for deep learning based diagnostic classification of Alzheimer’s disease: Application to metabolome data

Taeho Jo, Junpyo Kim, Paula Bice, Kevin Huynh, Tingting Wang, Peter J Meikle, Rima Kaddurah-Daouk, Kwangsik Nho, Andrew J. Saykin, AAIC (2022) We used serum-based cross-sectional lipidome data with 78...

Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification

Taeho Jo, Kwangsik Nho, Paula Bice, Andrew J Saykin, For The Alzheimer’s Disease Neuroimaging Initiative, Briefings in Bioinformatics (2022) We propose a novel three-step approach (SWAT-CNN) for...

Deep learning–based genome-wide association analysis in Alzheimer’s disease

Taeho Jo, Kwangsik Nho, Andrew J. Saykin, AAIC (2021) We used genome-wide genotyping data (12,448,786 SNPs following imputation) from 916 participants in the Alzheimer’s Disease Neuroimaging Ini...

Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

Taeho Jo, Kwangsik Nho, Shannon L. Risacher & Andrew J. Saykin for the Alzheimer’s Neuroimaging Initiative, BMC Bioinformatics (2020) We developed a deep learning-based framework to identify...

Deep learning detection of informative features in [18F] flortaucipir PET for Alzheimer’s disease classification

Taeho Jo, Kwangsik Nho, Shannon L. Risacher, Andrew J. Saykin, AAIC (2020) We downloaded 458 tau PET images (196 CN, 196 MCI, and 66 AD) from the Alzheimer’s Disease Neuroimaging Initiative (ADN...

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Taeho Jo, Kwangsik Nho, Andrew J. Saykin, Frontiers in Aging Neuroscience (2019) The application of deep learning to early detection and automated classification of AD has recently gained considerable...

Multimodal-3DCNN: Diagnostic Classification of Alzheimer's Disease Using Deep Learning on Neuroimaging, Genetic, and Demographic Data

Taeho Jo, Kwangsik Nho, Shannon L. Risacher, Andrew J. Saykin, AAIC (2019) Demographic information, 3D MRI and PET image data, and APOE data were downloaded from the ADNI data repository (N=329; 185 C...

Multimodal-CNN: Improved Accuracy of MRI-based Classification of Alzheimer’s Disease by Incorporating Clinical Data in Deep Learning

Taeho Jo, Kwangsik Nho, Shannon L. Risacher, Jingwen Yan, Andrew J. Saykin, AAIC (2018) Intermediate layers of the CNN were extracted, and the patient's clinical information was added by the gram ...

Evaluation of Protein Structural Models Using Random Forests

Renzhi Cao, Taeho Jo, Jianlin Cheng, arXiv (2016) We propose a new protein quality assessment method which can predict both local and global quality of the protein 3D structural models. Our method use...

Improving Protein Fold Recognition by Deep Learning Networks

Taeho Jo, Jie Hou, Jesse Eickholt & Jianlin Cheng, Scientific Reports (2015) The three–dimensional structure of Heterosigma akashiwo Na+–ATPase (HANA) was predicted by means of homolog...

Improving protein fold recognition by random forest

Taeho Jo & Jianlin Cheng, BMC Bioinformatics (2014) RF-Fold consists of hundreds of decision trees that can be trained efficiently on very large datasets to make accurate predictions on a highly i...

Homology Modeling of an Algal Membrane Protein, Heterosigma Akashiwo Na^+-ATPase

Taeho Jo, Mariko Shono, Masato Wada, Sayaka Ito, Junko Nomoto, Yukichi Hara, Membrane (2010) The three–dimensional structure of Heterosigma akashiwo Na+–ATPase (HANA) was predicted by mean...