Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation is a research paper published in Cell Systems (2021). On theSindex it has a DataRank of 0.644. It has been cited 72 times.
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Base Score Contribution
0.644
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Science Foundation
Grant: 2017-201
National Science Foundation
Grant: 2018-522
National Science Foundation
Grant: 2027790
RAPID: Collaborative: REACT: Real-time Contact Tracing and Risk Monitoring via Privacy-enhanced Mobile Tracking
National Institutes of Health
Grant: RR180012
National Human Genome Research Institute
Grant: R13HG009072
Fonds Wetenschappelijk Onderzoek
Grant: GOH9718N
Institute for Information and Communications Technology Promotion
Grant: 2020-0-00840
NIGMS
Grant: R01GM114612
ERC
Grant: ERC-2015-AdG-IMPaCT
National Institutes of Health
Grant: 5R13HG009072-04
iDASH Genome Privacy and Security Workshop (secure genome analysis competition)
Ulsan National Institute of Science and Technology
Ministry of Science and ICT, South Korea
NSF
FWCI
4.49
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Storing and analyzing a genome on a blockchain
Additional file 1 of Storing and analyzing a genome on a blockchain
Additional file 2 of Storing and analyzing a genome on a blockchain
Additional file 2 of Storing and analyzing a genome on a blockchain
Additional file 1 of Evaluation of vicinity-based hidden Markov models for genotype imputation
Additional file 1 of Evaluation of vicinity-based hidden Markov models for genotype imputation
Additional file 1 of Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility
Additional file 1 of Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility