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An approachable, flexible, and practical machine learning workshop for biologists

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.02.03.479008Source: DataRank Database

An approachable, flexible, and practical machine learning workshop for biologists is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 2 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access2 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 (18)IDs (PubMed, 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 (6)

Fangzhou Mu ORCID,Rosemary S. Russ ORCID,Milica Cvetkovic,Debora Treu,Anthony Gitter ORCID

Live enrichment

PubMed:19965766 OpenAlex:https://openalex.org/W4210246967

National Institutes of Health

Grant: 3T15LM007359-06S2

Research Training for Computation and Informatics in Biology and Medicine

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/03/18/2022.02.03.479008.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.02.03.479008

repositoryhttps://doi.org/10.1101/2022.02.03.479008

https://doi.org/10.1093/bioinformatics/btac233

Fields of Study

Genetics, Bioinformatics, and Biomedical ResearchCell Image Analysis TechniquesGene expression and cancer classification0206 medical engineering02 engineering and technology

Keywords

WorkflowComputer scienceArtificial intelligenceMachine learningActive learning (machine learning)FluencySoftware engineeringMathematics educationPsychologyDatabaseISCB/Ismb 2022HumansSoftware