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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...
Sparad:
| I publikationen: | BMC Med Genomics |
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| Huvudupphovsmän: | , , , , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
BioMed Central
2021
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7818577/ https://ncbi.nlm.nih.gov/pubmed/33472626 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12920-020-00869-9 |
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