Lah, James J.

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

Consensus meta-analysis of genome-wide association studies for Alzheimer’s disease and related dementias

EADB, Bellenguez, C., Castillo Morales, A., Amin, N., van der Lee, S. J., Muntaner, M., Parveen, K., Küçükali, F., Grenier-Boley, B., Heikkinen, S., et al. (2026) · Nature Genetics

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 7

Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits

Tian, G., Hanfelt, J., Lah, J., & Risk, B. B. (2024) · Data Science in Science

Bayesian Methods and Mixture ModelsDementia and Cognitive Impairment Research

Cited by 5