Missing Data Evaluation

Missing Data Evaluation is a methodological framework within proteomic quality control that systematically assesses the extent, patterns, and potential biases of absent or undetected protein abundance measurements in mass spectrometry-based datasets to ensure data integrity and analytical validity.

69

Publications

6.3K

Citations

380

Authors

177

Institutions

Publications per year

2017–2026

42

Authors

380

  1. SJ
    Sarah J. Parker

    Cedars-Sinai Medical Center, United States

    3 publications · 78 citations

    3

    h-index

  2. JQ
    Jun Qu

    University at Buffalo, State University of New York, United States

    3 publications · 303 citations

    3

    h-index

  3. BW
    Bobbie‐Jo Webb‐Robertson

    Pacific Northwest National Laboratory, United States

    3 publications · 214 citations

    3

    h-index

  4. KM
    Katrina M. Waters

    Pacific Northwest National Laboratory, United States

    3 publications · 214 citations

    3

    h-index

  5. TC
    Tianlu Chen

    Shanghai Jiao Tong University, China

    2 publications · 756 citations

    2

    h-index

1–5 of 380

Venues

  1. Journal of Proteome Research

    JPR · 10 publications · 618 citations

    10

    h-index

  2. Bioinformatics

    6 publications · 1.5K citations

    6

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 5 publications · 271 citations

    5

    h-index

  4. Scientific Reports

    4 publications · 903 citations

    4

    h-index

  5. Analytical Chemistry

    4 publications · 171 citations

    4

    h-index

1–5