Sparse Count Data Modeling

Sparse Count Data Modeling is a statistical framework for analyzing high-dimensional count data characterized by an excess of zero values, where the modeling approach specifically accounts for sparsity to enable accurate inference in single-cell genomic measurements.

28

Publications

3.1K

Citations

77

Authors

54

Institutions

Publications per year

2017–2026

16

Authors

77

  1. HL
    Hongzhe Li

    University of Pennsylvania, United States

    2 publications · 33 citations

    2

    h-index

  2. MM
    Maria Mircea

    Leiden University, Netherlands

    2 publications · 1.2K citations

    2

    h-index

  3. PW
    Paul W. Holland

    Educational Testing Service, United States

    2 publications · 170 citations

    2

    h-index

  4. NS
    Nikola S. Mueller

    Helmholtz Zentrum München, Germany

    2 publications · 1.2K citations

    2

    h-index

  5. LM
    Lukas M. Simon

    Baylor College of Medicine, United States

    2 publications · 1.2K citations

    2

    h-index

1–5 of 77

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 492 citations

    3

    h-index

  2. 3

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 2 publications · 115 citations

    2

    h-index

  4. Bioinformatics

    1 publications · 79 citations

    1

    h-index

  5. 1

    h-index

1–5