Dropout Modeling

Dropout Modeling is a computational method in single-cell genomics that specifically addresses the phenomenon of false negative gene expression measurements by statistically distinguishing biological zero expression from technical dropouts through probabilistic modeling.

632

Publications

43K

Citations

2.2K

Authors

866

Institutions

Publications per year

2017–2026

435

Authors

2.2K

  1. DS
    Debarka Sengupta

    Indraprastha Institute of Information Technology Delhi, India

    4 publications · 307 citations

    4

    h-index

  2. SZ
    Shihua Zhang

    Academy of Mathematics and Systems Science, China

    4 publications · 214 citations

    4

    h-index

  3. SM
    Somshubra Majumdar

    University of Illinois Chicago, United States

    3 publications · 158 citations

    3

    h-index

  4. XQ
    Xiaoning Qian

    Texas A&M University, United States

    3 publications · 47 citations

    3

    h-index

  5. AK
    Alex Kendall

    University of Cambridge, United Kingdom

    3 publications · 287 citations

    3

    h-index

1–5 of 2.2K

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 1.8K citations

    20

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 17 publications · 572 citations

    15

    h-index

  3. Bioinformatics

    14 publications · 1.2K citations

    13

    h-index

  4. IEEE Access

    10 publications · 504 citations

    9

    h-index

  5. Applied Sciences

    8 publications · 269 citations

    8

    h-index

1–5