Learning epistatic polygenic phenotypes with Boolean interactions is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2020). On theSindex it has a DataRank of 0.460. It has been cited 5 times, with 4 citing works in its 1-hop citation network.
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.
Base Score Contribution
0.269
From this paper's citation signal
Citation Network Contribution
0.191
From 4 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 Science Foundation
Grant: 1741340
BIGDATA: F: Scalable and Interpretable Machine Learning: Bridging Mechanistic and Data-Driven Modeling in the Biological Sciences
National Institutes of Health
Grant: 5T15LM007033-36
Biomedical Informatics Training at Stanford
Deutsche Forschungsgemeinschaft
Grant: unidentified
unidentified
Fields of Study
Keywords
Sustainable Development Goals