Sparse Marker Models

Sparse Marker Models is a statistical modeling approach in genomic prediction that belongs to the class of marker-based regression methods, distinguished by its deliberate selection and retention of only a small subset of genetic markers with nonzero effects to improve prediction accuracy and interpretability in the presence of high-dimensional, sparse genetic data.

15

Publications

1.2K

Citations

67

Authors

39

Institutions

Publications per year

2017–2026

4

Authors

67

  1. TM
    T.H.E. Meuwissen

    Norwegian University of Life Sciences, Norway

    2 publications · 70 citations

    2

    h-index

  2. AW
    Anthony Wilder Wohns

    University of Oxford, United Kingdom

    2 publications · 52 citations

    2

    h-index

  3. PS
    Pouria Salehi Nowbandegani

    Broad Institute, United States

    2 publications · 52 citations

    2

    h-index

  4. ES
    Eric S. Lander

    Broad Institute, United States

    2 publications · 52 citations

    2

    h-index

  5. LJ
    Luke J. O’Connor

    Broad Institute, United States

    2 publications · 52 citations

    2

    h-index

1–5 of 67

Venues

  1. BMC Bioinformatics

    2 publications · 80 citations

    2

    h-index

  2. Genetics Selection Evolution

    GSE · 2 publications · 70 citations

    2

    h-index

  3. ACM Transactions on Graphics

    TOG · 1 publications · 355 citations

    1

    h-index

  4. Biometrical Journal

    1 publications · 14 citations

    1

    h-index

  5. Journal of Animal Science

    1 publications · 196 citations

    1

    h-index

1–5