No description, website, or topics provided.
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data_prep_tricks
demography Update README.md Jul 22, 2018
historical_recombination
introgression
selection
tajimas_D
theta
LICENSE Initial commit Jul 27, 2017
README.md
sort.min.diff.py

README.md

Population genetic inference with Convolutional Neural Networks

This directory contains code for training and testing the neural networks described in this paper: The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference by Lex Flagel, Yaniv Brandvain, and Daniel Schrider. https://doi.org/10.1101/336073

Each folder contains the code and README files for a separate problem discussed in the paper. All code is presented as is and runs in python 2.7 unless stated otherswise. Code is specific to the problems described in the paper, but can be modified to address other problems in population genetics. The folders called tajimas_D and data_prep_tricks were not used in the paper above, but were used in the development of ideas presented in the paper. If you are looking for a good starting place to play with convolutional neural networks, check out the tajimas_D directory. It's a very simple model that will run in a minute or two on a laptop. The rest of the code in this repo will require a computer with significant amounts of RAM (~64 GB) and Tensorflow/Keras combined with a GPU (we used on an Nvidia K80).