<p>The complex interactions among genes severely influence human diseases. Therefore, understanding these interactions plays a crucial role in revealing facts about the dynamics of genes and diseases. Traditional analysis methods for diseases and genes are efficient, but they are limited in their ability to deeply investigate the interrelations and interactions among genes. Hence, this work involves concepts inspired by complex networks for modeling gene interactions. A dataset that includes the 80 most frequent human diseases from the National Center for Biotechnology Information (NCBI) is used. Two network models are generated, the first one comprises the chromosomes and their associated diseases, called Chromosome Network (CN). The second comprises 386 human genes associated with 80 diseases and called the Gene Interaction Network (GIN). The former is visualized and analyzed to extract facts about the relations among chromosomes and their associated diseases. The latter, which is the focus of this research, is deeply investigated by extracting its statistical mechanics. To this end, four structural features are involved: (i) <i>Percolation,</i> which is used to analyze the global structure of the GIN and show its robustness and resilience. (ii) <i>Triadic Closure</i> to analyze the local structure of the GIN as well as identify the strength of disease associations. (iii) <i>Influence Maximization</i> to explore the most influential genes in disease spreading. (iv) <i>Network Motifs</i> to distinguish the most common basic structural patterns. The results show crucial facts that might support biologists and clinicians with a deep understanding of diseases and gene processes alongside their functional pathways.</p>