Regression-based Deconvolution

Regression-Based Deconvolution is a computational method in cell-type deconvolution that employs regression models to estimate the proportional contributions of distinct cell types within a bulk tissue sample by leveraging gene expression signatures as predictors.

18

Publications

528

Citations

79

Authors

36

Institutions

Publications per year

2017–2026

11

Authors

79

  1. ML
    Michael Liebling

    University of California, Santa Barbara, United States

    1 publications · 25 citations

    1

    h-index

  2. RS
    Russell S. Crosbie

    CSIRO Land and Water, Australia

    1 publications · 42 citations

    1

    h-index

  3. AD
    Antonio D’ Andrilli

    Magna Graecia University, Italy

    1 publications · 19 citations

    1

    h-index

  4. MS
    Michael S. Weingarten

    Drexel University, United States

    1 publications · 27 citations

    1

    h-index

  5. CR
    Cyril Ruckebusch

    Centre National de la Recherche Scientifique, France

    1 publications · 22 citations

    1

    h-index

1–5 of 79

Venues

  1. Ground Water

    GW · 2 publications · 151 citations

    2

    h-index

  2. Genome biology

    1 publications · 23 citations

    1

    h-index

  3. European Journal of Pharmaceutical Sciences

    1 publications · 23 citations

    1

    h-index

  4. Analytical Chemistry

    1 publications · 22 citations

    1

    h-index

  5. Neurophotonics

    1 publications · 43 citations

    1

    h-index

1–5