Graph-regularized dual Lasso for robust eQTL mapping is a research paper published in UNC Libraries (2020). On theSindex it has a DataRank of 0.
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.
National Science Foundation
Grant: 1162374
III: Medium: Collaborative Research: Toward Robust and Scalable Discovering of Significant Associations in Massive Genetic Data
National Science Foundation
Grant: 1218036
III: Small: Mining Local Correlations in Extremely High-Dimensional Data: Models, Algorithms, and Applications
National Institutes of Health
Grant: 5P50GM076468-05
Core--Computational
National Institutes of Health
Grant: 1R01HG006703-01
Imputation and Analysis of Rare Variants in Admixed Populations
National Science Foundation
Grant: 1313606
III: Medium: Collaborative Research: Toward Robust and Scalable Discovering of Significant Associations in Massive Genetic Data
FWCI
0.00
Citation Percentile
0.3%
Fields of Study
Keywords