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
Parents
632
Single-cell RNA-seq denoising using a deep count autoencoder
Gökçen Eraslan, Lukas M. Simon, Maria Mircea et al. · Nature Communications · 2019 · 1.2K citations · Full text
1–5 of 632
2.2K
Indraprastha Institute of Information Technology Delhi, India
4 publications · 307 citations
4
h-index
4
h-index
3
h-index
3
h-index
3
h-index
1–5 of 2.2K
866

Chinese Academy of Sciences
Beijing, China
12 h-index
31 publications
3K citations
Tsinghua University
Beijing, China
12 h-index
41 publications
4.4K citations
University of Chinese Academy of Sciences
Beijing, China
8 h-index
14 publications
2.3K citations
University of Science and Technology of China
Hefei, China
7 h-index
24 publications
1.4K citations
University of Michigan
Ann Arbor, United States
6 h-index
11 publications
761 citations
University of California, Los Angeles
Los Angeles, United States
6 h-index
15 publications
2.1K citations

King Saud University
Riyadh, Saudi Arabia
6 h-index
11 publications
905 citations

Beihang University
Beijing, China
5 h-index
10 publications
281 citations
1–8 of 866
Repository · 37 publications · 1.8K citations
20
h-index
Repository · 17 publications · 572 citations
15
h-index
14 publications · 1.2K citations
13
h-index
10 publications · 504 citations
9
h-index
8 publications · 269 citations
8
h-index
1–5