A deep learning framework for designing switch-like core promoters

DeepSwitch is a deep learning framework that integrates predictive and generative models to efficiently design switch-like core promoter sequences. Using massively parallel reporter assays, we profile 48,000 natural and synthetic sequences in inducible and cell type-specific enhancer contexts. This repository provides access to the datasets and trained models for both training and evaluation purposes.