New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy is a research paper published in Patterns (2021). On theSindex it has a DataRank of 0.568. It has been cited 43 times.
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Base Score Contribution
0.568
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: 1P01CA22551701
National Institutes of Health
Grant: R01GM118417
National Institutes of Health
Grant: UM1CA15496708
National Cancer Institute
Grant: P30-CA015704
NCI NIH HHS
Grant: P30 CA015704
NCI NIH HHS
Grant: K24 CA139052
NCI NIH HHS
Grant: P01 CA225517
NCI NIH HHS
Grant: UM1 CA154967
NCI NIH HHS
Grant: U01 CA154967
National Institutes of Health
Grant: 1R01GM118417-01A1
Big Flow Cytometry Data: Data Standards, Integration and Analysis
National Institutes of Health
Grant: 3P30CA015704-42S2
Cancer Center Support Grant
National Institutes of Health
Grant: 5K24CA139052-07
Pathogenetic and prognostic studies for improved therapy of Merkel cell carcinoma
National Institutes of Health
Grant: 3U01CA154967-06S2
Cancer Immunotherapy Trials Network Central Operations and Statistical Center
Celgene
Janssen Pharmaceuticals
Merck
Juno Therapeutics
Takeda Pharmaceutical Company
Fields of Study
Keywords
Sustainable Development Goals