Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.485. It has been cited 9 times, with 8 citing works in its 1-hop citation network.
Scored on demand from live citation data
Repositories this paper deposited data in (declared in PubMed).
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.
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Base Score Contribution
0.345
From this paper's citation signal
Citation Network Contribution
0.140
From 7 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 8 citers.
National Institutes of Health
Grant: 5R01LM013337-04
Modeling the Incompleteness and Biases of Health Data
National Institutes of Health
Grant: 1U01TR003528-01A1
CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning
Fields of Study
Keywords
Sustainable Development Goals