A deep learning model leveraging semantic features fusion for DNase I hypersensitive sites identification in the human genome

This study presents that the model integrates semantic vector-based feature fusion representation, which effectively captures both local and global patterns with inherited spatio-temporal dependencies within complex DHSs sequences. The model's performance was validated both with and without semantic feature fusion, followed by quantitative and statistical analyses against individual models, significantly enhancing feature representation and classification performance. Source code and datasets are available at: https://github.com/malikmtahir/DNase/tree/main.