Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation

Abstract Background Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias-correction methods face limitations in handling the heterogeneity of biomedical da...

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Detalhes bibliográficos

Principais autores:	Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu, Yonggang Zhou, Xiaohui Ni, Mary L. Stackpole, Angela H. Yeh, Andrew Melehy, David S. Lu, Steven S. Raman, William Hsu, Lopa Mishra, Kirti Shetty, Benjamin Tran, Megumi Yokomizo, Preeti Ahuja, Yazhen Zhu, Hsian-Rong Tseng, Denise R. Aberle, Vatche G. Agopian, Steven-Huy B. Han, Samuel W. French, Steven M. Dubinett, Xianghong Jasmine Zhou, Wing Hung Wong
Formato:	Artigo
Idioma:	Inglês
Publicado em:	BMC 2026-02-01
coleção:	Genome Biology
Assuntos:	Demographic bias cfDNA methylation Bias correction Machine learning Cancer detection
Acesso em linha:	https://doi.org/10.1186/s13059-026-04006-0
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