TongjiZhanglab/chrombert-human-200bp

ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks

Model description

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 model is human and 1kb resolution

This version of the model is trained for human data at 200 bp resolution.