Risacher, Shannon L.

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

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Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer’s disease

Xie, L., He, B., Varathan, P., Nho, K., Risacher, S. L., Saykin, A. J., Salama, P., & Yan, J. (2021) · Briefings in Bioinformatics

Bioinformatics and Genomic NetworksAlzheimer's disease research and treatments

Cited by 16

A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression

Bae, J., Logan, P. E., Acri, D. J., Bharthur, A., Nho, K., Saykin, A. J., Risacher, S. L., Nudelman, K., Polsinelli, A. J., Pentchev, V., et al. (2023) · Alzheimer's & Dementia

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 14