Sequence-Only Prediction of Super-Enhancers in Human Cell Lines Using Transformer Models.

ABSTRACT: The study discloses the application of transformer-based deep learning models for the task of super-enhancers prediction in human tumor cell lines with a specific focus on sequence-only features within studied entities of super-enhancer and enhancer elements in the human genome. The proposed SE-prediction method included the GENA-LM application at handling long DNA sequences with the classification task, distinguishing super-enhancers from enhancers using H3K36me, H3K4me1, H3K4me3 and H3K27ac landscape datasets from HeLa, HEK293, H2171, Jurkat, K562, MM1S and U87 cell lines. The model was fine-tuned on relevant sequence data, allowing for the analysis of extended genomic sequences without the need for epigenetic markers as proposed in early approaches. The study achieved balanced accuracy

SUBMITTER: Kravchuk EV 

PROVIDER: S-EPMC11852244 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature