MeNet is a mixed-effects deep neural network architecture for multi-environment agronomic traits prediction. This repository includes model deployment, training steps, prediction steps, and model parameter settings.

Set up environments using the following command:

conda create -n MeNet python=3.9.17
conda activate MeNet
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt 

• The genotype data and phenotype datasets should be in a data folder, organizing as follows:

  + data
      + gene
      	++ genotype.csv
      + phen 
       	++ phenotype.csv
      + splits
      	

• To generate genetic relatedness, train the trait specific encoder of the RepGeno. The resulting genetic relatedness is then saved in save/genetic_relatedness.pt.

  python train_trait_specific_encoder.py [parameters]
  
  [parameters]:
      -m --margin      # Contrastive loss margin (distance threshold)
      -p --phen_name   # Name of the target phenotype
      -d --device      # Device to use: 'cuda' or 'cpu'
      -f --flag				 #  Sampling mode: 0 for trait-based, 1 for population-based positive/negative pairs

  The parameter settings are provided in configs/contrastive_learning.json. The constructed genetic relatedness will have a significant impact on the prediction performance of MeNet.

• To train the MeNet model and evaluate the contributions of the VE and RepGeno modules, use the following command. The error tracking results during training and the contributions of the VE and RepGeno modules will be saved in menet.log

  python menet.py [parameters] > menet.log 
  
  [parameters]:
      -p --phen_name   # Name of the target phenotype
      -w --windows   # Windows mechanism 0: don't use windows_mechanism, 1: windows_mechanism by chromosome
      -d --device      # Device to use: 'cuda' or 'cpu'

​ The parameter settings are provided in configs/MeNet.json.

• To apply transfer learning, you can fine-tune a pre-trained model by selectively freezing and unfreezing specific layers. The following code snippet demonstrates how to configure the model after loading a pre-trained model:

  # Freeze all parameters in the model
  for param in model.parameters():
      param.requires_grad = False
      
  # Unfreeze 
  for param in model.fusion.field_for_ve.parameters():
      param.requires_grad = True
  for param in model.fusion.output.parameters():
      param.requires_grad = True

  Explanation

  • Freezing the model: Setting requires_grad = False for all parameters prevents them from being updated during training, preserving the pre-trained weights.
  • Selective unfreezing: Specific layers are set to requires_grad = True to allow fine-tuning.
  • Ensure the pre-trained model is loaded before applying these settings.

This work is based on pytorch , scikit-learn, and plink. The project is developed by following author and supervised by Prof. Xiangchao Gan(gan@njau.edu.cn)

Authors:

Yanhui Li (huiyl@stu.njau.edu.cn): prototype development, data processing, model validation

Shengjie Ren (sunflower@stu.njau.edu.cn): overall framework design, contrastive learning strategy design, feature fusion mechanism, model architecture formulation, and interpretability and weight contribution analysis