Marker Density Optimization

Marker Density Optimization is a methodological approach in genomic prediction that determines the optimal number and distribution of genetic markers required to maximize predictive accuracy while minimizing genotyping costs and computational burden.

24

Publications

1.5K

Citations

134

Authors

68

Institutions

Publications per year

2017–2026

6

Authors

134

  1. TV
    Todd Vision

    University of North Carolina at Chapel Hill, United States

    2 publications · 180 citations

    2

    h-index

  2. XW
    Xiaoping Wu

    China Agricultural University, China

    1 publications · 59 citations

    1

    h-index

  3. TB
    Timothy Beissinger

    University of Wisconsin–Madison, United States

    1 publications · 219 citations

    1

    h-index

  4. AH
    Aladdin Hamwieh

    International Center for Agricultural Research in the Dry Areas, Syria

    1 publications · 18 citations

    1

    h-index

  5. RD
    Raphaël Dumas

    Université Claude Bernard Lyon 1, France

    1 publications · 16 citations

    1

    h-index

1–5 of 134

Venues

  1. Genetics

    4 publications · 458 citations

    4

    h-index

  2. BMC Genomics

    2 publications · 40 citations

    2

    h-index

  3. Genetics Selection Evolution

    GSE · 2 publications · 139 citations

    2

    h-index

  4. Crop Science

    1 publications · 235 citations

    1

    h-index

  5. ACTA AGRONOMICA SINICA

    1 publications · 10 citations

    1

    h-index

1–5