GeoGenIE: a deep learning approach to predict geographic provenance of biodiversity samples from genomic SNPs

Authors

Bradley T. Martin (ORCID: https://orcid.org/0000-0002-3014-4692)
Tyler K. Chafin (ORCID: https://orcid.org/0000-0001-8687-5905)
Michael E. Douglas (ORCID: https://orcid.org/0000-0001-9670-7825)
Marlis R. Douglas (ORCID: https://orcid.org/0000-0001-6234-3939)
Zachary D Zbinden

Institutions

Seton Hall University (US)
University of Maryland Center for Environmental Science (US)
Wellcome Sanger Institute (GB)
Biomathematics and Statistics Scotland (GB)
University of Arkansas at Fayetteville (US)

Publication Details

Journal: Bioinformatics Advances
Published: 2024-12-26
DOI: https://doi.org/10.1093/bioadv/vbaf250
Primary Topic: Environmental DNA in Biodiversity Studies
Type: article
Field-Weighted Citation Impact: 0.00

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article

GeoGenIE: a deep learning approach to predict geographic provenance of biodiversity samples from genomic SNPs

Bradley T. Martin, Tyler K. Chafin, Michael E. Douglas, Marlis R. Douglas et al.

Dec 26, 2024

Bioinformatics Advances

Environmental DNA in Biodiversity Studies

article

GeoGenIE: a deep learning approach to predict geographic provenance of biodiversity samples from genomic SNPs

Bradley T. Martin, Tyler K. Chafin, Michael E. Douglas, Marlis R. Douglas, Zachary D Zbinden

December 26, 2024article en

Abstract

No abstract available for this paper.

Bioinformatics AdvancesVol. 5(1)

Seton Hall University (US), University of Maryland Center for Environmental Science (US), Wellcome Sanger Institute (GB), Biomathematics and Statistics Scotland (GB), University of Arkansas at Fayetteville (US)

Openalex Percentile: Top 72%

Environmental DNA in Biodiversity Studies

DOI Open Access PDF OpenAlex

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