A novel deep learning framework with dynamic tokenization for identifying chromatin interactions along with motif importance investigation.

ABSTRACT: A comprehensive understanding of chromatin interaction networks is crucial for unraveling the regulatory mechanisms of gene expression. While various computational methods have been developed to predict chromatin interactions and address the limitations and high costs of high-throughput experimental techniques, their performance is often overestimated due to the specificity of chromatin interaction data. In this study, we proposed Inter-Chrom, a novel deep learning model integrating dynamic tokenization, DNABERT's word embedding, and the efficient channel attention mechanism to identify chromatin interactions using sequence and genomic features, leveraging a newly curated dataset. Experimental results demonstrate that Inter-Chrom outperforms existing methods on three cell line datasets. Ad

SUBMITTER: Li L 

PROVIDER: S-EPMC12204613 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature