The increasing interest in the non-protein-coding portion of the genome is one of the most challenging aspects to comprehend the most basic interactions of our DNA, which are almost entirely unknown deeply. The primary purpose of this thesis is to exploit the active parts of the Promoters to predict the Gene Expression value related to the protein-coding genes of Lung Healthy tissues. Therefore, I implemented a Deep Learning approach based on a custom-designed Convolutional Neural Network (CNN). The promoters are empirically associated with their genes, defining a new method to merge the gene to its regulatory part. This thesis also investigates the possible influence of other regulatory elements that can affect the final expression value.