PyTorch implementation of the paper:
Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks (paper)
Based on the paper and the original implementation in lua language (GitHub page), Basset provides researchers with tools to:
- Train deep convolutional neural networks to learn highly accurate models of DNA sequence activity such as accessibility (via DNaseI-seq or ATAC-seq), protein binding (via ChIP-seq), and chromatin state.
- Interpret the principles learned by the model.
This project is currently undergoing completion.