Author

Stephen Hermes

Virginia Energy

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:polygenic risk· 1 Approach:statistical model· 1

Frequent Co-Authors

Carlos CruchagaiD· 1 shared Ellen McRae GreytakiD· 1 shared James O’Connor· 1 shared Janet Cady· 1 shared Sarah E. CarlsoniD· 1 shared Steven L. Armentrout· 1 shared The Alzheimer’s Disease Neuroimaging Initiative· 1 shared Thomas S. WingoiD· 1 shared

Activity by Year

2023-2023 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 28 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Carlos CruchagaORCID

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1 paper

Recent·Most cited

Epistatic Features and Machine Learning Improve Alzheimer's Risk Prediction Over Polygenic Risk Scores

Stephen Hermes, Janet Cady, Steven L. Armentrout, James O’Connor, Sarah E. Carlson, Carlos Cruchaga, Thomas S. Wingo, Ellen McRae Greytak, The Alzheimer’s Disease Neuroimaging Initiative

This study develops a paragenic risk score that improves Alzheimer's disease risk prediction by incorporating epistatic interactions and machine learning, outperforming traditional polygenic risk scores.

medRxiv · Feb 10, 2023 · PreprintGenetic & Genomic Medicine