Modelpublic

jruffle/ae-tracerx-256d

sourceHugging Facemitupdated 1y agoView on Hugging Face

0likes

Model Card

Autoencoder (TRACERx-focused, 256D)

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

Model Details

—Model Type: Autoencoder
—Dataset: TRACERx-focused
—Latent Dimensions: 256
—Compression Mode: transcriptome
—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, 256]
—Output: 256-dimensional latent representation
—Activation: ELU with batch normalization

Training Data

Trained exclusively on TRACERx open dataset

Files

—autoencoder_256_latent_dims_oos_mode.pt: Main model weights
—latent_df.csv: Example latent representations (if available)