ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages is a research paper published in Bioinformatics (2020). On theSindex it has a DataRank of 0.542. It has been cited 36 times.
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Base Score Contribution
0.542
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: R01AG067025
National Institutes of Health
Grant: R21CA237955
National Institutes of Health
Grant: U01MH116492
National Institutes of Health
Grant: U54HD090256
NCI NIH HHS
Grant: K22 CA188169
National Institutes of Health
Grant: 5R01AG067025-04
Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease
National Institutes of Health
Grant: 1R21CA237955-01A1
A new bladder cancer model based on tissue reprogramming and gene targeting
National Institutes of Health
Grant: 5U54HD090256-05
Waisman Intellectual and Developmental Disabilities Research Center
European Commission
Grant: 237955
Fast Analog Computing with Emergent Transient States - Initial Training Network (FACETS-ITN)
National Institutes of Health
Grant: 5U01MH116492-02
1/2 Discovery and validation of neuronal enhancers associated with the development of psychiatric disorders
Waisman Center
FWCI
1.34
Citation Percentile
0.8%
Citation Trend
Fields of Study
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Keywords
Sustainable Development Goals