Variant Recalibration
Variant Recalibration is a computational method in genomic variant analysis that belongs to the class of post-processing techniques, distinguished by its use of machine learning models to re-evaluate and adjust variant quality scores based on multiple covariates and empirical data distributions.
8
Publications
830
Citations
82
Authors
25
Institutions
Publications per year
2017–2026
3
Variants
Vqsr
Parents
8
High-Throughput Reclassification of SCN5A Variants
Andrew M. Glazer, Yuko Wada, Bian Li et al. · The American Journal of Human Genetics · 2020 · 152 citations · Full text
1–5 of 8
82
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Memorial Sloan Kettering Cancer Center, United States
1 publications · 116 citations
1
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1–5 of 82
25

Vanderbilt University
Nashville, United States
1 h-index
2 publications
304 citations
Harvard University
Cambridge, United States
1 h-index
1 publications
116 citations
HUN-REN Research Centre for Natural Sciences
Budapest, Hungary
1 h-index
4 publications
128 citations
Vanderbilt University Medical Center
Nashville, United States
1 h-index
11 publications
1.7K citations
Auckland UniServices (New Zealand)
Auckland, New Zealand
1 h-index
1 publications
373 citations
Myriad Genetics
Salt Lake City, United States
1 h-index
12 publications
516 citations
Max Planck Institute for Evolutionary Anthropology
Leipzig, Germany
1 h-index
2 publications
24 citations

Cornell University
Ithaca, United States
1 h-index
1 publications
116 citations
1–8 of 25
2 publications · 44 citations
2
h-index
1 publications · 116 citations
1
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1 publications · 43 citations
1
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1 publications · 373 citations
1
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1 publications · 21 citations
1
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1–5