Paving the path toward genomic privacy with secure imputation

Maxwell A Sherman¹

Affiliations

Affiliation

¹ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Harvard-MIT Health Sciences and Technology Program, Cambridge, MA, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: maxas@mit.edu.

PMID: 34672957
DOI: 10.1016/j.cels.2021.09.006

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Paving the path toward genomic privacy with secure imputation

Maxwell A Sherman. Cell Syst. 2021.

. 2021 Oct 20;12(10):950-952.

doi: 10.1016/j.cels.2021.09.006.

Author

Maxwell A Sherman¹

Affiliation

¹ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Harvard-MIT Health Sciences and Technology Program, Cambridge, MA, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: maxas@mit.edu.

PMID: 34672957
DOI: 10.1016/j.cels.2021.09.006

Abstract

Ensuring the privacy of participants in genomic studies is a critical responsibility of the biomedical community. Accurate and efficient implementations of secure genotype imputation highlight practical approaches to safeguard sensitive genomic data that can be adapted for numerous bioinformatics applications.

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Paving the path toward genomic privacy with secure imputation

Affiliation

Paving the path toward genomic privacy with secure imputation

Author

Affiliation

Abstract

Comment on

Publication types

MeSH terms

Grants and funding