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Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.03.01.529396Source: DataRank Database

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0. It has been cited 6 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access6 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 (11)IDs (PubMed)

FAIR Checklist

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Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (2)

Adam Siepel ORCID,Ziyi Mo ORCID

Live enrichment

PubMed:19162232

National Institutes of Health

Grant: 1R35GM127070-01

Evolutionary Human Genomics: Demography, Natural Selection, and Transcriptional Regulation

greenLicense: cc-by-nc

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/03/01/2023.03.01.529396.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.03.01.529396

repositoryhttps://repository.cshl.edu/id/eprint/41071/1/2023_Mo_Domain-adaptive_neural_networks_improve_preprint.pdf

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10002701/pdf/nihpp-2023.03.01.529396v2.pdf

https://doi.org/10.1371/journal.pgen.1011032

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Machine LearningGenetics, PopulationGeneticsComputer SimulationNeural Networks, ComputerSupervised Machine LearningQH426-470ArticleResearch Article