Data from: The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference
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2017-10-1
2018-5-10
2018-5-10
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2018-06-10
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Flagel, Lex
flag0010@gmail.com
flag0010@gmail.com
Abstract
Large neural network training data sets. The corresponding code for training can be found here: https://github.com/flag0010/pop_gen_cnn
Both the data sets and code are associated with this paper: https://www.biorxiv.org/content/early/2018/05/31/336073
Description
Each file is a numpy "npz" file. More details here: https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html
And contains separate test, validate, and training data sets, for both the input data and response.
Referenced by
Flagel, Lex, Yaniv J. Brandvain, and Daniel R. Schrider. “The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.” bioRxiv preprint, Jan 1, 2018.
https://doi.org/10.1101/336073
https://doi.org/10.1101/336073
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CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
http://creativecommons.org/publicdomain/zero/1.0/
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Flagel, Lex; Brandvain, Yaniv; Schrider, Daniel. (2018). Data from: The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/D65M4P.
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Readme_CNN.txt
Description of data
(5.91 KB)
ld.data.npz
training data for phased chromosomes neural network
(122.51 MB)
autotet.ld.data.npz
Training dataset for autotetraploid recombination
(352.2 MB)
big_sim.npz
training and validation set for introgression model
(333.36 MB)
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