Prediction and interpretation of cancer survival using graph convolution neural networks is a research paper published in Methods (2021). On theSindex it has a DataRank of 0.621. It has been cited 62 times.
Scored on demand from live citation data
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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
0.621
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 →NIGMS NIH HHS
Grant: R01 GM113245
NCI NIH HHS
Grant: K99 CA248944
NCATS NIH HHS
Grant: UL1 TR002645
NCATS NIH HHS
Grant: UM1 TR004538
NCI NIH HHS
Grant: P30 CA054174
National Institutes of Health
Grant: 1UL1TR002645-01
Institute for Integration of Medicine & Science: A Partnership to Improve Health
National Institutes of Health
Grant: 1R01GM113245-01
Collaborative Research:Graphical models for characterizing global RNA methylation
National Institutes of Health
Grant: 5P30CA054174-16
SAN ANTONIO CANCER INSTITUTE
National Institutes of Health
Grant: 1K99CA248944-01
Deep learning of drug sensitivity and genetic dependency of pediatric cancer cells
National Institutes of Health
National Cancer Institute
Cancer Prevention and Research Institute of Texas
FWCI
3.78
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals