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

Authors

82

  1. GA
    Guruprasad Ananda

    Jackson Laboratory, United States

    1 publications · 116 citations

    1

    h-index

  2. AJ
    Adam J. Schoenfeld

    Memorial Sloan Kettering Cancer Center, United States

    1 publications · 116 citations

    1

    h-index

  3. VR
    Vincent Rennie

    University of Antwerp, Belgium

    1 publications · 21 citations

    1

    h-index

  4. LE
    Laura E. MacConaill

    Dana-Farber Cancer Institute, United States

    1 publications · 116 citations

    1

    h-index

  5. SG
    Stefan Groha

    Dana-Farber Cancer Institute, United States

    1 publications · 116 citations

    1

    h-index

1–5 of 82

Venues

  1. BMC Genomics

    2 publications · 44 citations

    2

    h-index

  2. Cancer Cell

    1 publications · 116 citations

    1

    h-index

  3. Breast Cancer Research and Treatment

    1 publications · 43 citations

    1

    h-index

  4. Frontiers in Pharmacology

    1 publications · 373 citations

    1

    h-index

  5. PLoS Computational Biology

    1 publications · 21 citations

    1

    h-index

1–5