QuantitativeBiology/GainPro

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.

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

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.

Usage

To use the pre-trained model for inference:

1. 1.Install the required dependencies:

   pip install torch numpy