Computational Pipeline Optimization

Computational Pipeline Optimization is a methodological framework within label-free quantification that systematically enhances the efficiency, accuracy, and reproducibility of computational workflows by refining algorithmic parameters, data processing steps, and resource allocation strategies specific to mass spectrometry-based proteomic analyses.

34

Publications

1.8K

Citations

222

Authors

112

Institutions

Publications per year

2017–2026

24

Authors

222

  1. CC
    Charlie C. L. Wang

    Chinese University of Hong Kong, Hong Kong SAR China

    2 publications · 62 citations

    2

    h-index

  2. HR
    Harikrishna Rallapalli

    New York University, United States

    1 publications · 18 citations

    1

    h-index

  3. MP
    Mihika Pradhan

    DKFZ-ZMBH Alliance, Germany

    1 publications · 115 citations

    1

    h-index

  4. TZ
    Tianyu Zhang

    University of Manchester, United Kingdom

    1 publications · 32 citations

    1

    h-index

  5. NH
    Nina Huber

    Stanford University, United States

    1 publications · 40 citations

    1

    h-index

1–5 of 222

Venues

  1. Nucleic Acids Research

    2 publications · 152 citations

    2

    h-index

  2. BMC Genomics

    2 publications · 145 citations

    2

    h-index

  3. ACM Transactions on Graphics

    TOG · 2 publications · 62 citations

    2

    h-index

  4. Bioinformatics

    2 publications · 82 citations

    2

    h-index

  5. BMC Bioinformatics

    1 publications · 65 citations

    2

    h-index

1–5