Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic is a research paper published in PLoS Computational Biology (2022). On theSindex it has a DataRank of 1.5. It has been cited 57 times, with 49 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.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.609
From this paper's citation signal
Citation Network Contribution
0.938
From 35 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 49 citers.
Foundation for the National Institutes of Health
Grant: R35 GM122547
National Institutes of Health
Grant: 3R35GM122547-02S1
Extracting rich information from biological images
FWCI
12.80
Citation Percentile
1.0%
Citation Trend
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