Modeling autosomal dominant Alzheimer’s disease with machine learning

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer’s disease. METHODS: Longitudinal structural MRI, amyloid PET, and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Al...

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Publicado no:Alzheimers Dement
Main Authors: Luckett, Patrick H., McCullough, Austin, Gordon, Brian A., Strain, Jeremy, Flores, Shaney, Dincer, Aylin, McCarthy, John, Kuffner, Todd, Stern, Ari, Meeker, Karin L., Berman, Sarah B., Chhatwal, Jasmeer P., Cruchaga, Carlos, Fagan, Anne M., Farlow, Martin R., Fox, Nick C., Jucker, Mathias, Levin, Johannes, Masters, Colin L., Mori, Hiroshi, Noble, James M., Salloway, Stephen, Schofield, Peter R., Brickman, Adam M., Brooks, William S., Cash, David M., Fulham, Michael J., Ghetti, Bernardino, Jack, Clifford R., Vöglein, Jonathan, Klunk, William, Koeppe, Robert, Oh, Hwamee, Su, Yi, Weiner, Michael, Wang, Qing, Swisher, Laura, Marcus, Dan, Koudelis, Deborah, Joseph-Mathurin, Nelly, Cash, Lisa, Hornbeck, Russ, Xiong, Chengjie, Perrin, Richard J., Karch, Celeste M., Hassenstab, Jason, McDade, Eric, Morris, John C., Benzinger, Tammie L.S., Bateman, Randall J., Ances, Beau M.
Formato: Artigo
Idioma:Inglês
Publicado em: 2021
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Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC8195816/
https://ncbi.nlm.nih.gov/pubmed/33480178
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/alz.12259
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