Predicting Alzheimer's disease progression using deep recurrent neural networks is a research paper published in NeuroImage (2020). On theSindex it has a DataRank of 0.807. It has been cited 216 times.
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
0.807
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 →National Institutes of Health
Grant: U01 AG024904
U.S. Department of Defense
Grant: W81XWH-12-2-0012
NCRR NIH HHS
Grant: S10 RR023401
NCRR NIH HHS
Grant: S10 RR023043
NIBIB NIH HHS
Grant: P41 EB015896
NCRR NIH HHS
Grant: S10 RR019307
H. Lundbeck A/S
Johnson and Johnson
University of Southern California
Fujirebio Europe
IXICO
Nvidia
Merck
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
GE Healthcare
Genentech
Northern California Institute for Research and Education
Alzheimer's Disease Neuroimaging Initiative
CIHR
Janssen Research and Development
National Institute of Biomedical Imaging and Bioengineering
National Research Foundation Singapore
National Institute on Aging
CIHR
FWCI
14.31
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease
Additional file 1 of Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease