Jo, Taeho

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

Jo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience

Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

Cited by 724

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

Jo, T., Nho, K., Bice, P., Saykin, A. J., & Initiative, F. T. A. D. N. (2022) · Briefings in Bioinformatics

Genetic Associations and EpidemiologyAlzheimer's disease research and treatments

Cited by 80

White matter abnormalities and cognition in aging and Alzheimer disease

Peter, C., Sathe, A., Shashikumar, N., Pechman, K. R., Workmeister, A. W., Jackson, T. B., Huo, Y., Mukherjee, S., Mez, J., Dumitrescu, L. C., et al. (2025) · JAMA Neurology

Dementia and Cognitive Impairment ResearchAdvanced Neuroimaging Techniques and Applications

Cited by 59

Towards cascading genetic risk in Alzheimer’s disease

Altmann, A., Aksman, L. M., Oxtoby, N. P., Young, A. L., ADNI, Weiner, M., Aisen, P., Petersen, R., Weiner, M., Aisen, P., et al. (2024) · Brain

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 18

Deep learning to quantify the pace of brain aging in relation to neurocognitive changes

Yin, C., Imms, P., Chowdhury, N. F., Chaudhari, N. N., Ping, H., Wang, H., Bogdan, P., Irimia, A., Initiative, T. A. D. N., Weiner, M., et al. (2025) · Proceedings of the National Academy of Sciences

Dementia and Cognitive Impairment ResearchAdvanced MRI Techniques and Applications

Cited by 18

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

Jo, T., Kim, J., Bice, P., Huynh, K., Wang, T., Arnold, M., Meikle, P. J., Giles, C., Kaddurah-Daouk, R., Saykin, A. J., et al. (2023) · eBioMedicine

Dementia and Cognitive Impairment ResearchMetabolomics and Mass Spectrometry Studies

Cited by 15