Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity

Jessica Binder; Oleg Ursu; Cristian Bologa; Shanya Jiang; Nicole Maphis; Somayeh Dadras; Devon Chisholm; Jason Weick; Orrin Myers; Praveen Kumar; Jeremy J. Yang; Kiran Bhaskar; Tudor I. Oprea

doi:10.1038/s42003-022-03068-7

Communications Biology (Feb 2022)

Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity

Jessica Binder,
Oleg Ursu,
Cristian Bologa,
Shanya Jiang,
Nicole Maphis,
Somayeh Dadras,
Devon Chisholm,
Jason Weick,
Orrin Myers,
Praveen Kumar,
Jeremy J. Yang,
Kiran Bhaskar,
Tudor I. Oprea

Affiliations

Jessica Binder: Department of Internal Medicine, University of New Mexico School of Medicine
Oleg Ursu: Department of Internal Medicine, University of New Mexico School of Medicine
Cristian Bologa: Department of Internal Medicine, University of New Mexico School of Medicine
Shanya Jiang: Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine
Nicole Maphis: Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine
Somayeh Dadras: Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine
Devon Chisholm: Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine
Jason Weick: Department of Neuroscience, University of New Mexico School of Medicine
Orrin Myers: Department of Internal Medicine, University of New Mexico School of Medicine
Praveen Kumar: Department of Internal Medicine, University of New Mexico School of Medicine
Jeremy J. Yang: Department of Internal Medicine, University of New Mexico School of Medicine
Kiran Bhaskar: Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine
Tudor I. Oprea: Department of Internal Medicine, University of New Mexico School of Medicine

DOI: https://doi.org/10.1038/s42003-022-03068-7
Journal volume & issue: Vol. 5, no. 1
pp. 1 – 15

Abstract

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Jessica Binder et al. developed a machine learning model to discover potential drug targets for Alzheimer’s disease. They validated their 20 top candidates in several in vitro models, and highlight FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2 as potential AD risk genes.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

About the journal