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• Version 1.0 -First version released on August, 20th, 2022

DNNGP requires Python 3.6. Follow the instructions at https://www.tensorflow.org/install/gpu to set up GPU support for faster model training. Once GPU is set up, install with conda by executing these instructions from the root of the checked-out repository:

conda create -n dnngp python=3.6.5
conda activate dnngp
pip install -r requirements.txt

Users can also use DNNGP on CPU, and the installation method is the same as above.

Download the release package and unzip to your working directory.

To run locally, there are two required input files. One file contains the phenotype of interest, the other file contains the SNP data, genomic expression data or other related omics data with digital coding.

An example command to train DNNGP to predict the phenotype pheno from the SNP data, genomic expression data or other related omics data with digital coding is the following:

python dnngp_runner.py \
  --batch_size [num] \
  --epoch [num] \
  --lr [num] \
  --patience [num] \
  --dropout1 [num] \
  --dropout2 [num] \
  --SNP [your omics-data file] \
  --pheno [your phenotype data file]

Of particular note is the run.py. This script is used to get results in batches by adjusting different hyperparameters and inputs.

The example-data files are alread included in the release package,

You can also get the exmample data from source code. Download example-data.tgz and extract data with tar zxvf example-data.tgz.

• tomato332
• wheat599
• maize1404
• wheat2000

@Kelin Wang

@Huihui Li

Kelin Wang（[email protected]）

Huihui Li（[email protected]）

GPLv3 © Kelin Wang, Huihui Li