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Clinical and Multiomic Features Differentiate Young Black and White Breast Cancer Cohorts Derived by Machine Learning Approaches

Clinical Breast Cancer(2024)10.1016/j.clbc.2024.11.015Source: DataRank Database

Clinical and Multiomic Features Differentiate Young Black and White Breast Cancer Cohorts Derived by Machine Learning Approaches is a research paper published in Clinical Breast Cancer (2024). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 0.00)OA: greenTopics (35)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Boris Aguilar ORCID,George K. Acquaah-Mensah ORCID,Kawther Abdilleh

Live enrichment

PubMed:39706709 PMC:PMC11911081 OpenAlex:https://openalex.org/W4404794186

NCI NIH HHS

Grant: HHSN261201500003C

NCI NIH HHS

Grant: HHSN261201500003I

NCI NIH HHS

Grant: HHSN261201400008C

FWCI

0.00

Citation Percentile

0.2%

green

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11911081/

journalhttps://doi.org/10.1016/j.clbc.2024.11.015

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39706709

Fields of Study

Breast Cancer Treatment StudiesAI in cancer detectionCancer Risks and Factors

MeSH Terms

Machine LearningWhiteAdultBlack or African AmericanBreast NeoplasmsFemaleHumansMiddle AgedPrognosisBiomarkers, TumorUnited StatesCohort StudiesDNA MethylationGene Expression ProfilingWhite People

Keywords

MedicineBreast cancerWhite (mutation)OncologyCancerBioinformaticsArtificial intelligenceComputational biologyInternal medicineGeneticsBiologyGeneCohort AnalysisHealth DisparitiesBiclusteringMulti-omicsBreast Cancer Molecular Subtype