Autoencoder (General Purpose, 64D)
This model is part of the TRACERx Datathon 2025 transcriptomics analysis pipeline.
Model Details
- Model Type: Autoencoder
- Dataset: General Purpose
- Latent Dimensions: 64
- 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, 64]
- Output: 64-dimensional latent representation
- Activation: ELU with batch normalization
Training Data
Trained on broader open transcriptomics datasets
Files
autoencoder_64_latent_dims_oos_mode.pt: Main model weightslatent_df.csv: Example latent representations (if available)
