jruffle/ae_general_32d

Autoencoder (General Purpose, 32D)

This model is part of the TRACERx Datathon 2025 transcriptomics analysis pipeline.

Model Details

• Model Type: Autoencoder
• Dataset: General Purpose
• Latent Dimensions: 32
• Compression Mode: samples
• Framework: PyTorch

Usage

This model is designed to be used with the TRACERx Datathon 2025 analysis pipeline. It will be automatically downloaded and cached when needed.

Model Architecture

• Input: Gene expression data
• Hidden layers: [input_size, 512, 256, 128, 32]
• Output: 32-dimensional latent representation
• Activation: ELU with batch normalization

Training Data

Trained on broader open transcriptomics datasets

Files

• autoencoder_32_latent_dims_oos_mode.pt: Main model weights
• latent_df.csv: Example latent representations (if available)