SAILER: scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration is a research paper published in Bioinformatics (2021). On theSindex it has a DataRank of 0. It has been cited 18 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
NSF
Grant: IIS-1715017
NSF
Grant: DMS-1763272
NIH
Grant: U54-CA217378
NIMH
Grant: K01 MH123896
Simons Foundation
Grant: 594598
NCI NIH HHS
Grant: U54 CA217378
National Science Foundation
Grant: 1763272
NSF-Simons Center for Multiscale Cell Fate Research
National Institutes of Health
Grant: 5U54CA217378-05
Complexity, Cooperation and Community in Cancer
National Science Foundation
Grant: 1715017
III: Small: Integrating and Interpreting Heterogeneous Genomic Data Through Deep Learning
National Institutes of Health
Grant: 5K01MH123896-02
A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders
FWCI
1.11
Citation Percentile
0.8%
Citation Trend
Fields of Study
MeSH Terms
Keywords