Autoencoder (TRACERx-focused, 32D)
This model is part of the TRACERx Datathon 2025 transcriptomics analysis pipeline.
Model Details
- Model Type: Autoencoder
- Dataset: TRACERx-focused
- 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 exclusively on TRACERx open dataset
Files
autoencoder_32_latent_dims_oos_mode.pt: Main model weightslatent_df.csv: Example latent representations (if available)
