| title: Autoencoder General Purpose 3D | |
| emoji: 🧬 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: pytorch | |
| tags: | |
| - transcriptomics | |
| - dimensionality-reduction | |
| - ae | |
| - general | |
| license: mit | |
| # Autoencoder (General Purpose, 3D) | |
| This model is part of the TRACERx Datathon 2025 transcriptomics analysis pipeline. | |
| ## Model Details | |
| - **Model Type**: Autoencoder | |
| - **Dataset**: General Purpose | |
| - **Latent Dimensions**: 3 | |
| - **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, 3] | |
| - Output: 3-dimensional latent representation | |
| - Activation: ELU with batch normalization | |
| ## Training Data | |
| Trained on broader open transcriptomics datasets | |
| ## Files | |
| - `autoencoder_3_latent_dims_oos_mode.pt`: Main model weights | |
| - `latent_df.csv`: Example latent representations (if available) | |