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Enabling interpretable machine learning for biological data with reliability scores

PLoS Computational Biology(2023)10.1371/journal.pcbi.1011175Source: DataRank Database

Enabling interpretable machine learning for biological data with reliability scores is a research paper published in PLoS Computational Biology (2023). On theSindex it has a DataRank of 0. It has been cited 23 times.

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Open Access23 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (8 grants)OA: goldTopics (12)IDs (PubMed, PMC)

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Authors (3)

Lauren A. Sugden ORCID,Sohini Ramachandran ORCID,K. D. Ahlquist ORCID

Live enrichment

PubMed:37235578 PMC:PMC10249903

NIH

Grant: R01GM118652

NIH

Grant: R35GM139628

NIH

Grant: 5T32GM007601

NIGMS NIH HHS

Grant: T32 GM007601

National Institutes of Health

Grant: 1R35GM139628-01

Novel population-genetic methods for localizing targets of natural selection in diverse human genomes

National Institutes of Health

Grant: 5T32GM007601-07

MOLECULAR AND CELL BIOLOGY

National Institutes of Health

Grant: 1R01GM118652-01

Novel statistical methods to localize genomic elements underlying adaptive evolution

Wimmer Family Foundation

goldLicense: cc-by

publisherhttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011175&type=printable

publisherhttps://dx.plos.org/10.1371/journal.pcbi.1011175

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10249903/pdf/pcbi.1011175.pdf

repositoryhttps://doaj.org/article/e64e8ab025c045458a7b1f0cbf59cd09

Unpaywallhttps://doi.org/10.1371/journal.pcbi.1011175

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

Reproducibility of ResultsGenomeMachine Learning

Keywords

Machine LearningGenomeQH301-705.5Reproducibility of ResultsBiology (General)Research Article