Predicting 3D Chromatin Interactions Using Transformer-Enhanced Deep Learning Models.

The three-dimensional (3D) structure of the human genome is essential for regulating gene expression and cellular functions. Chromatin interactions bring distant genomic regions into physical contact, enabling processes like gene regulation, DNA replication, and repair. Disruptions in this organization can lead to diseases such as cancer and genetic disorders. In this study, we propose a Transformer-based deep learning model to predict the chromatin interactions from DNA sequences. By developing a streamlined and efficient data pipeline to handle the sparse and noisy high-throughput chromosome conformation capture (Hi-C) sequencing data, our approach improves both data processing speed and model performance. The Transformer's ability to capture long-range interactions among genomic regions via attention mechanism, combined with nucleotide position encoding, enables more accurate predictions than purely convolution-based models. This work highlights the potential of Transformer-based network architectures to advance our understanding of genome organization and paves the way for future research with large datasets and advanced network designs.