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license: bsd-3-clause
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# DANN & GAIN Hybrid Model 

This repository contains the GAIN-DANN hybrid model, which is a hybrid model that combines:
- **Generative Adversarial Imputation Networks (GAIN)** for missing values imputation 

- **Domain-Adversarial Neural Networks (DANN)** for domain-aware learning and classification. 

The model is designed for proteomics data and has been pre-trained on a subset of the HeLa dataset.

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## Model Architecture 

Key components:
- `Encoder`: Maps input to latent space
- `Generator` & `Discriminator`: For adversarial imputation
- `Gradient Reversal Layer`: Enables domain adversarial training
- `Domain Classifier`: Predicts domain labels
- `Decoder`: Reconstructs inputs after imputation

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## Model Configuration

The pre-trained model (`pytorch_model.bin`) has been trained on the HeLa dataset with the following configuration:

- Input Dimension: 3013
- Latent Dimension: 3013
- Number of Classes: 17
- Hidden Dimension: 128
- Dropout: 0.3

The model weights are stored in `pytorch_model.bin`, and the configuration is provided in `config.json`.

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## Usage
To use the pre-trained model for inference:

1. Install the required dependencies:
   ```bash
   pip install torch numpy