Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging is a research paper published in Communications Biology (2024). On theSindex it has a DataRank of 0.561. It has been cited 41 times.
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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
0.561
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →U.S. Department of Health & Human Services | NIH | National Institute on Aging
Grant: U01 AG070112-01A1
NINDS NIH HHS
Grant: R01 NS121154
NEI NIH HHS
Grant: R01 EY032768
NIA NIH HHS
Grant: U01 AG070112
NCATS NIH HHS
Grant: UL1 TR003167
National Institutes of Health
Grant: 3UL1TR003167-02S4
Center for Clinical and Translational Sciences (CCTS)
National Institutes of Health
Grant: 5R01NS121154-03
Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
National Institutes of Health
Grant: 3U01AG070112-02S3
Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
FWCI
14.26
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals