Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0.312. It has been cited 7 times.
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
0.312
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: 1ZIAAG000534-03
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Grant: 1Z01AG000949-01
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National Institutes of Health
Grant: 5U54GM114838-02
KnowEng, a Scalable Knowledge Engine for Large-Scale Genomic Data-OVERALL
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Grant: 1ZIANS003154-09
Genetic characterization of atypical parkinsonism
Fields of Study
Keywords
Sustainable Development Goals