CAESAR is a deep learning model that predicts 3D chromatin organization from 1D DNA sequences. It can predict Hi-C contact maps, ChIA-PET interactions, and chromatin accessibility profiles. The model is trained on data from multiple cell types and can be used to predict chromatin organization in new cell types.

The model uses a transformer-based architecture to capture long-range dependencies in DNA sequences. It is trained on data from the ENCODE project and can be used to predict chromatin organization in any cell type. The model is available as a Python package and can be used with minimal setup.