End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins is a research paper published in PLOS Computational Biology (2023). On theSindex it has a DataRank of 0.793. It has been cited 21 times, with 13 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?
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Base Score Contribution
0.464
From this paper's citation signal
Citation Network Contribution
0.329
From 11 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 13 citers.
National Institute of General Medical Sciences
Grant: R01GM122845
National Institute on Aging
Grant: R01AD057555
National Science Foundation
Grant: 2226183
AI-powered multi-scale modeling of microbiome-host interactions
NIA NIH HHS
Grant: R01 AG057555
National Institutes of Health
Grant: 2R01GM122845-05
AI-powered chemical proteomics for drug discovery targeting orphan proteins
National Institutes of Health
Grant: 5R01AI057555-06
TpI2 Kinase in Macrophage Activation by Microbes
FWCI
2.97
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
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Sustainable Development Goals