Epigenomic Data Normalization

Epigenomic Data Normalization is a computational procedure that belongs to the class of data preprocessing methods, specifically designed to adjust high-throughput epigenomic measurements for systematic technical variations while preserving biologically meaningful patterns across samples.

30

Publications

9.1K

Citations

160

Authors

77

Institutions

Publications per year

2017–2026

11

Authors

160

  1. KD
    Kasper D. Hansen

    Johns Hopkins University, United States

    4 publications · 2K citations

    4

    h-index

  2. AL
    Aurélie Labbe

    McGill University, Canada

    3 publications · 1.1K citations

    3

    h-index

  3. CM
    Celia M.T. Greenwood

    McGill University, Canada

    3 publications · 1.1K citations

    3

    h-index

  4. BW
    Brent W. Zanke

    Ontario Institute for Cancer Research, Canada

    2 publications · 1K citations

    2

    h-index

  5. GD
    George Davey Smith

    University of Bristol, United Kingdom

    2 publications · 395 citations

    2

    h-index

1–5 of 160

Venues

  1. Bioinformatics

    9 publications · 5.6K citations

    9

    h-index

  2. Genome biology

    4 publications · 2K citations

    4

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 4 publications · 114 citations

    4

    h-index

  4. Methods in molecular biology

    Book series · 2 publications · 48 citations

    2

    h-index

  5. Frontiers in Genetics

    2 publications · 41 citations

    2

    h-index

1–5