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Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.10.26.564244Source: DataRank Database

Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0. It has been cited 4 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access4 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (1 grants)OA: greenTopics (20)IDs (PubMed, OpenAlex)SDGs (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)

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Authors (2)

Facundo M. Fernández ORCID,Olatomiwa O. Bifarin ORCID

Live enrichment

PubMed:37961534 OpenAlex:https://openalex.org/W4388036439

National Institutes of Health

Grant: 1R01CA218664-01A1

Deep Ovarian Cancer Metabolomics

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/10/31/2023.10.26.564244.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.10.26.564244

repositoryhttps://doi.org/10.1101/2023.10.26.564244

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37961534

Fields of Study

Metabolomics and Mass Spectrometry StudiesCancer, Hypoxia, and MetabolismFerroptosis and cancer prognosis

Keywords

MetabolomicsInterpretabilityMachine learningArtificial intelligenceComputer sciencePipeline (software)Feature (linguistics)BiologyBioinformaticsOvarian NeoplasmsBiomarkers, TumorHumansFemaleCarcinoma, Renal CellArticleKidney NeoplasmsAlgorithms

Sustainable Development Goals

SDG 0: Reduced inequalities