Data from: The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference

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https://doi.org/10.13020/D65M4P
https://hdl.handle.net/11299/198335

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Keywords

deep neural network

population genetics

Collection period

2017-10-1
2018-5-10

Date completed

2018-06-10

Date updated

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Journal Title

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Volume Title

Title

Data from: The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference

Published Date

2018-07-25

Authors

Flagel, Lex

Brandvain, Yaniv

Schrider, Daniel

Group

Department of Plant and Microbial Biology

Author Contact

Flagel, Lex
flag0010@gmail.com

Type

Dataset
Simulation Data

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

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CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/

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Data Repository for U of M (DRUM)

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Previously Published Citation

Suggested citation

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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