High performance logistic regression for privacy-preserving genome analysis

BACKGROUND: In biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by comb...

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Detalles Bibliográficos
Autores principales: De Cock, Martine, Dowsley, Rafael, Nascimento, Anderson C. A., Railsback, Davis, Shen, Jianwei, Todoki, Ariel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Technical Advance
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818577/
https://www.ncbi.nlm.nih.gov/pubmed/33472626
http://dx.doi.org/10.1186/s12920-020-00869-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818577/
https://www.ncbi.nlm.nih.gov/pubmed/33472626
http://dx.doi.org/10.1186/s12920-020-00869-9

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