Equitable machine learning counteracts ancestral bias in precision medicine, improving outcomes for all

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease [1–8]. Therapeutics and outcomes remain hidden because we lack insights that we could gain from analyzing ancestry-unbiased genomic data. To address th...

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Detalles Bibliográficos
Autores principales: Smith, Leslie A, Cahill, James A, Graim, Kiley
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402189/
https://www.ncbi.nlm.nih.gov/pubmed/37546907
http://dx.doi.org/10.21203/rs.3.rs-3168446/v1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402189/
https://www.ncbi.nlm.nih.gov/pubmed/37546907
http://dx.doi.org/10.21203/rs.3.rs-3168446/v1

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