Imputation Of Missing Values

Imputation of Missing Values is a computational method that belongs to the class of data preprocessing techniques and is distinguished by its specific purpose of estimating unobserved or undetected metabolite abundances in single-cell metabolomics datasets using statistical, probabilistic, or machine learning models informed by biological and technical patterns of data sparsity.

1.9K

Publications

215K

Citations

5.5K

Authors

2K

Institutions

Publications per year

2017–2026

838

Authors

5.5K

  1. IR
    Ian R. White

    MRC Biostatistics Unit, United Kingdom

    30 publications · 13K citations

    26

    h-index

  2. JR
    James R. Carpenter

    London School of Hygiene & Tropical Medicine, United Kingdom

    25 publications · 11K citations

    23

    h-index

  3. DB
    Donald B. Rubin

    Harvard University, United States

    20 publications · 8.8K citations

    18

    h-index

  4. SV
    Stef van Buuren

    Netherlands Organisation for Applied Scientific Research, Netherlands

    22 publications · 25.3K citations

    18

    h-index

  5. PR
    Patrick Royston

    MRC Clinical Trials Unit at UCL, United Kingdom

    19 publications · 15.6K citations

    18

    h-index

1–5 of 5.5K

Venues

  1. Statistics in Medicine

    81 publications · 8.3K citations

    40

    h-index

  2. Journal of the American Statistical Association

    39 publications · 10.4K citations

    28

    h-index

  3. Bioinformatics

    29 publications · 14.2K citations

    26

    h-index

  4. BMC Bioinformatics

    31 publications · 2.4K citations

    25

    h-index

  5. BMC Medical Research Methodology

    32 publications · 6K citations

    23

    h-index

1–5