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The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference

View ORCID ProfileLex Flagel, View ORCID ProfileYaniv Brandvain, View ORCID ProfileDaniel R. Schrider
doi: https://doi.org/10.1101/336073
Lex Flagel
1Monsanto Company, Chesterfield, MO.
2Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN.
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  • ORCID record for Lex Flagel
Yaniv Brandvain
2Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN.
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Daniel R. Schrider
3Department of Genetics, University of North Carolina, Chapel Hill, NC.
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  • For correspondence: drs@unc.edu
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Article Information

doi 
https://doi.org/10.1101/336073
History 
  • November 27, 2018.

Article Versions

  • Version 1 (May 31, 2018 - 15:40).
  • Version 2 (October 22, 2018 - 13:58).
  • You are viewing Version 3, the most recent version of this article.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

Author Information

  1. Lex Flagel1,2,
  2. Yaniv Brandvain2 and
  3. Daniel R. Schrider3,*
  1. 1Monsanto Company, Chesterfield, MO.
  2. 2Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN.
  3. 3Department of Genetics, University of North Carolina, Chapel Hill, NC.
  1. ↵*Corresponding author. Email: drs{at}unc.edu
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Posted November 27, 2018.
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The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference
Lex Flagel, Yaniv Brandvain, Daniel R. Schrider
bioRxiv 336073; doi: https://doi.org/10.1101/336073
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The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference
Lex Flagel, Yaniv Brandvain, Daniel R. Schrider
bioRxiv 336073; doi: https://doi.org/10.1101/336073

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