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+# ___DNNGP: Deep neural network for genomic prediction___ <br>
+<br>
+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.
+<br><br>
+
+Data used in the papers' example-data.tgz 'can be found in the package at [DNNGP-v1.0.0.zip](https://github.com/AIBreeding/DNNGP/releases/download/v1.0.0/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](DNNGP-usermanual.pdf).  
+  
+Tips:
+Deep learning models perform better with larger sample sizes.
+
+### Change log
+2025.01.21:  
+ 1. Updated the hyperparameter auto-tuning script `DNNGP_OPN.py`, which you can find in the `Tuning_hyperparameters` directory.
+ 2. `DNNGP_OPN.py` enables batch tuning of multi phenotype.
+    
+2024.03:
+ 1. Update the software to version 3.1 for both Windows and Linux.
+ 2. Optimize the naming of output files for model training. The current file name concatenates the `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.
+ 3. Optimize the complex parameter adjustment process.
+
+### It is suggested tuning parameters as follows:
+
+    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
+
+
+## 👥 Contacts
+
+[Huihui Li](lihuihui@caas.cn)（lihuihui@caas.cn）
+