Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning is a research paper published in PLoS ONE (2021). On theSindex it has a DataRank of 0.360. It has been cited 10 times.
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Base Score Contribution
0.360
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 Cancer Institute
Grant: R21 CA160157
National Institutes of Health
Grant: CA136829
National Institutes of Health
Grant: R01CA186769
National Institutes of Health
Grant: 1R01CA250499
National Institutes of Health
Grant: T32-DE007057
National Institutes of Health
Grant: T32 EB005582-05
NCI NIH HHS
Grant: R01 CA136829
NIBIB NIH HHS
Grant: T32 EB005582
NCI NIH HHS
Grant: R01 CA250499
NIDCR NIH HHS
Grant: T32 DE007057
National Institutes of Health
Grant: 5T32DE007057-42
Tissue Engineering and Regeneration
National Institutes of Health
Grant: 5R21CA160157-02
Magnetorotation: a Rapid Assay for Single Cell Drug Sensitivity of Cancer Cells
National Institutes of Health
Grant: 5R01CA136829-05
Microfluidic Models of Metastatic Cancer
National Institutes of Health
Grant: 5R01CA186769-03
Photonic Nanosonophores for Functional and Structural Imaging
National Institutes of Health
Grant: 2T32EB005582-11
Microfluidics in Biomedical Sciences Training Program
National Institutes of Health
Grant: 5R01CA250499-03
Personalized Cancer Therapy Guided by Photoacoustic Chemical Imaging (PACI) of Tumor Microenvironment (TME)
US Department of Education GAANN fellowship
FWCI
0.51
Citation Percentile
0.6%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals