ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks
ChromBERT is a pre-trained deep learning model designed to capture the genome-wide co-association patterns of approximately one thousand transcription regulators, thereby enabling accurate representations of context-specific transcriptional regulatory networks (TRNs). As a foundational model, ChromBERT can be fine-tuned to adapt to various biological contexts through transfer learning. This significantly enhances our understanding of transcription regulation and offers a powerful tool for a broad range of research and clinical applications in different biological settings.
This repository provides the checkpoints and required dependencies for ChromBERT.
For usage, see ChromBERT for detail.