A machine learning approach identifies cellular senescence on transcriptome data of human cells in vitro is a research paper published in GeroScience (2024). On theSindex it has a DataRank of 0.437. It has been cited 8 times, with 7 citing works in its 1-hop citation network.
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?
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Base Score Contribution
0.330
From this paper's citation signal
Citation Network Contribution
0.107
From 5 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 7 citers.
National Institutes of Health
Grant: 1P01HL160476-01A1
Mechanisms that Govern Cardiomyocyte Proliferation and Remuscularization following Ventricular Injury
National Institutes of Health
Grant: 1U54AG079754-01
Midwest Murine-Tissue Mapping Center (MM-TMC)
National Institutes of Health
Grant: 3P01AG062413-02S1
Targeting Cellular Senescence to Extend Healthspan
National Institutes of Health
Grant: 2T32AG029796-11
Functional Proteomics of Aging
National Institutes of Health
Grant: 1U54AG076041-01
Minnesota Tissue Mapping Center for Senescent Cells
National Institutes of Health
Grant: 5R00AG056656-06
Computational evaluation of the causal role of somatic mutations in human aging
National Institutes of Health
Grant: 6U19AG056278-03
Genetic variant-based drug discovery targeting conserved pathways of aging
FWCI
1.94
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals