The Python project 'DNNGP' can be used to implement genome-wide prediction (GP), which can predict the phenotypes of plants and animals based on multi-omics data. The code is written using Python 3.9 and TensorFlow 2.6.0.
Data used in the papers' example-data.tgz 'can be found in the package at DNNGP-v1.0.0.zip
The original data download address is as follows:
maize:
https://pan.baidu.com/s/1AsPJLTe--gU5EN8aFTMYPA
http://www.maizego.org/download/Agronomic_23Traits.txt
tomato:
https://www.ncbi.nlm.nih.gov/sra?term=SRP150040
https://www.ncbi.nlm.nih.gov/sra?term=SRP186721
https://www.ncbi.nlm.nih.gov/sra?term=SRP186721
wheat:
https://github.com/gdlc/BGLR-R/blob/master/data/wheat.RData
More information could be found in the user manual.
Tips: Deep learning models perform better with larger sample sizes.
2024.03:
input phenotype file name, the original output file name and the part parameter value. This change prevents the issue of overlapping phenotypic characters and fold number collisions with files.batchsize: Set this to the largest value your hardware can support, typically increasing powers of 2.
lr: Set this to 1, or any value you think is appropriate based on your understanding of deep learning. The learning rate is partially auto-adjusted by the internal algorithm.
epoch: Set a maximum value and allow “earlystopping” to decide the optimal stopping point.
dropout1；This parameter should be experimentally determined, with recommended trials ranging from 0.1 to 0.9.
dropout2: Similarly, this needs empirical evaluation, usually between 0.1 and 0.9.
patience: A value between 10 and 100 is generally acceptable. It doesn't take much adjustment.
earlystopping: Set this value to 5-10 times the value of patience. Increase this multiplier if the iterations end too quickly.The information above is consistent with our user manual. For more details, please refer to the user manual.
国内下载地址：https://datashare.biobin.com.cn/flask
Huihui Li（lihuihui@caas.cn）