Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer

Genome Research(2024)10.1101/gr.278565.123Source: DataRank Database

Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer is a research paper published in Genome Research (2024). On theSindex it has a DataRank of 0. It has been cited 14 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access14 citations

Download PDF

Cite:

Linked data & code

geo

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)OA: greenTopics (37)IDs (PubMed, PMC)Annotations (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (8)

Weitai Huang ORCID,Yu Amanda Guo ORCID,Dustin Shigaki,Shamaine Wei Ting Ho ORCID,Patrick Tan ORCID

Live enrichment

PubMed:38777607 PMC:PMC11216402

National Institutes of Health–National Human Genome Research Institute

Grant: R01 HG012367

National Medical Research Council

Grant: MOH-000967-00

National Medical Research Council

Grant: MOH-STaR21jun-0001

Open Fund-Large Collaborative

Grant: MOH-OFLCG18May-0003

NHGRI NIH HHS

Grant: R56 HG012110

National Institutes of Health

Grant: 5R01HG012367-02

Sequence-based Machine Learning for Inference of Dynamic Cell State Gene Network Models

National Research Foundation, Singapore, and Singapore Ministry of Health's National Medical Research Council

greenLicense: cc-by-nc

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11216402/pdf/680.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/gr.278565.123

Europe_PMChttps://europepmc.org/articles/PMC11216402

Europe_PMChttps://europepmc.org/articles/PMC11216402?pdf=render

https://doi.org/10.1101/gr.278565.123

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciencesHumansMachine LearningStomach NeoplasmsEpithelial-Mesenchymal TransitionGene Expression Regulation, NeoplasticTranscription Factor AP-1Cell Line, TumorFibrosisCore Binding Factor Alpha 1 SubunitDNA Copy Number VariationsCore Binding Factor Alpha 2 Subunit

MeSH Terms

Cell Line, TumorHumansStomach NeoplasmsFibrosisTranscription Factor AP-1Gene Expression Regulation, NeoplasticCore Binding Factor Alpha 1 SubunitCore Binding Factor Alpha 2 SubunitDNA Copy Number VariationsEpithelial-Mesenchymal TransitionMachine Learning

Keywords

Epithelial-Mesenchymal TransitionDNA Copy Number VariationsResearchCore Binding Factor Alpha 1 SubunitFibrosisMachine LearningGene Expression Regulation, NeoplasticTranscription Factor AP-1Stomach NeoplasmsCell Line, TumorCore Binding Factor Alpha 2 SubunitHumans

Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer

FAIR Checklist

Authors (8)

Funding (7 grants)

Open Access

Topics

Annotations (Europe PMC)