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Hyperbolic geometry-based deep learning methods to produce population trees from genotype data

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.03.28.484797Source: DataRank Database

Hyperbolic geometry-based deep learning methods to produce population trees from genotype data is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 3 times.

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Open Access3 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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

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Authors (4)

Daniel Mas Montserrat ORCID,Carlos D. Bustamante ORCID,Alexander G. Ioannidis ORCID,Aman Patel ORCID

Live enrichment

PubMed:19767382 OpenAlex:https://openalex.org/W4220682866

National Institutes of Health

Grant: 3U01HG009080-05S1

Optimizing imputation for diverse populations in a distributed framework

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/03/31/2022.03.28.484797.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.03.28.484797

repositoryhttps://doi.org/10.1101/2022.03.28.484797

Fields of Study

Genetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestockGenetic diversity and population structure0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Tree (set theory)Computer sciencePopulationAutoencoderBenchmark (surveying)Artificial intelligenceCluster analysisParametric statisticsMachine learningData miningDeep learningMathematicsStatisticsGeography