CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data is a research paper published in Cell Genomics (2025). On theSindex it has a DataRank of 0.328. It has been cited 4 times, with 4 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
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.
Base Score Contribution
0.241
From this paper's citation signal
Citation Network Contribution
0.0862
From 3 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 4 citers.
National Cancer Institute
Grant: R01CA260060
National Cancer Institute
Grant: K99/R00
NIGMS
Grant: R35GM138293
NHGRI
Grant: R00HG009679
National Institutes of Health
Grant: 4R00HG009679-02
Novel computational approaches for pharmacogenomic discovery
National Institutes of Health
Grant: 5R35GM138293-03
Computational tools for estimating cell-type-specific effects in bulk RNA-seq and spatial transcriptomics data, using reference single-cell RNA-seq datasets
National Institutes of Health
Grant: 5R01CA260060-05
Developing new therapeutic strategies for pediatric tumors that lack clinically actionable mutations
National Institutes of Health
American Lebanese Syrian Associated Charities
FWCI
1.59
Citation Percentile
0.8%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals