human-atac-catlas-model

Model Description

This model is a multi-task binary classifier trained to predict chromatin accessibility across 204 cell types. It was trained by fine-tuning the Enformer model using the grelu library on top of the CATlas human enhancer dataset.

• Architecture: Fine-tuned Enformer
• Input: Genomic sequences (hg38)
• Output: Binary accessibility predictions for 204 cell type tasks.

Repository Content

1. model.ckpt: The trained model weights and hyperparameters (PyTorch Lightning checkpoint).
2. 2_train.ipynb: Jupyter notebook containing the training logic, architecture definition, and evaluation loops.
3. output.log: Training logs.

How to use

To load this model for inference or fine-tuning, use the grelu interface:

from grelu.lightning import LightningModel
from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(
    repo_id="Genentech/human-atac-catlas-model", 
    filename="model.ckpt"
)

model = LightningModel.load_from_checkpoint(ckpt_path)
model.eval()