Abstract
<jats:p>Deep learning has emerged as a transformative tool in genomic sequence analysis, particularly in identifying regulatory elements like enhancers, promoters, and silencers. This article reviews state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers, and their applications in genomic tasks such as chromatin state prediction and motif discovery. Models like DeepSEA and DanQ have demonstrated exceptional performance, achieving AUC-ROC scores of 0.89 and 0.91, respectively. Despite significant progress, data scarcity, interpretability, and computational demands persist. The article discusses advancements like federated learning and model compression as potential solutions. It highlights the role of deep learning in integrating multi-omics data for personalized medicine and cancer genomics. These insights underline the potential of deep learning to revolutionize genomic research, paving the way for novel applications in healthcare and biological discovery.</jats:p>