Accounting for population structure in deep learning models for genomic analysis

While population structure may not heavily affect model performance, it is important to reduce the models' capabilities of shortcut learning when designing deep learning models for analyzing genomic datasets, by using ancestry-related variants over potentially relevant biomarkers of the disease or disorder in question. The code used to perform these analyses can be found at: https://github.com/notTrivial/populationStructure.

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Publication Details

Journal
Journal of Biomedical Informatics
Published
2025-07-05
DOI
https://doi.org/10.1016/j.jbi.2025.104873
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
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article

Accounting for population structure in deep learning models for genomic analysis

Raissa Souza, Nils D. Forkert, Gabrielle Dagasso, Matthias Wilms
1 links
Journal of Biomedical Informatics
Genetic Associations and Epidemiology
article

Accounting for population structure in deep learning models for genomic analysis

Raissa Souza, Nils D. Forkert, Gabrielle Dagasso, Matthias Wilms
article en

Abstract

While population structure may not heavily affect model performance, it is important to reduce the models' capabilities of shortcut learning when designing deep learning models for analyzing genomic datasets, by using ancestry-related variants over potentially relevant biomarkers of the disease or disorder in question. The code used to perform these analyses can be found at: https://github.com/notTrivial/populationStructure.

Journal of Biomedical InformaticsVol. 169
University of Calgary (CA), Alberta Children's Hospital (CA)
Openalex Percentile: Top 81%
Genetic Associations and Epidemiology
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