Understanding the complex regulation of gene expression by non-coding regions of the genome for cell-type-specific transcriptional activity is
very challenging. Such regulatory mechanisms are crucial for developing a deeper understanding of human diseases and potential therapeutic
strategies. Advancements in high-throughput sequencing technologies have enabled us to build deep learning models to predict gene
expression from DNA sequences. This research focuses on utilizing deep learning methods to predict cell-type-specific gene expression and
generate novel synthetic sequences for precise gene expression control.