Our paper contributes to understanding differential expression gene (DEG) signatures, a crucial element in the study of gene expression patterns. One of the key findings of our research, which has not been previously published, is the assertion that the DEG signature space significantly manifests hyperbolic properties. This discovery has far-reaching implications for both the fields of bioinformatics and machine learning, such as drug-target interaction prediction and visualizations for drug discovery.