Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity
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.
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-02-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-022-03068-7 |
| _version_ | 1819274336011288576 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Jessica Binder |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T23:06:48Z |
| format | Article |
| id | doaj.art-eafe528a0fb444148715ee8fe57ec23e |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-12-23T23:06:48Z |
| publishDate | 2022-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-eafe528a0fb444148715ee8fe57ec23e2022-12-21T17:26:48ZengNature PortfolioCommunications Biology2399-36422022-02-015111510.1038/s42003-022-03068-7Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunityJessica Binder0Oleg Ursu1Cristian Bologa2Shanya Jiang3Nicole Maphis4Somayeh Dadras5Devon Chisholm6Jason Weick7Orrin Myers8Praveen Kumar9Jeremy J. Yang10Kiran Bhaskar11Tudor I. Oprea12Department of Internal Medicine, University of New Mexico School of MedicineDepartment of Internal Medicine, University of New Mexico School of MedicineDepartment of Internal Medicine, University of New Mexico School of MedicineDepartment of Molecular Genetics and Microbiology, University of New Mexico School of MedicineDepartment of Molecular Genetics and Microbiology, University of New Mexico School of MedicineDepartment of Molecular Genetics and Microbiology, University of New Mexico School of MedicineDepartment of Molecular Genetics and Microbiology, University of New Mexico School of MedicineDepartment of Neuroscience, University of New Mexico School of MedicineDepartment of Internal Medicine, University of New Mexico School of MedicineDepartment of Internal Medicine, University of New Mexico School of MedicineDepartment of Internal Medicine, University of New Mexico School of MedicineDepartment of Molecular Genetics and Microbiology, University of New Mexico School of MedicineDepartment of Internal Medicine, University of New Mexico School of MedicineJessica 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.https://doi.org/10.1038/s42003-022-03068-7 |
| spellingShingle | 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 Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity Communications Biology |
| title | Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity |
| title_full | Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity |
| title_fullStr | Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity |
| title_full_unstemmed | Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity |
| title_short | Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity |
| title_sort | machine learning prediction and tau based screening identifies potential alzheimer s disease genes relevant to immunity |
| url | https://doi.org/10.1038/s42003-022-03068-7 |
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