Identification of Drugs and Association of Genes with Diseases among Anxiety, Bipolar Disorder, Heart Disease and Stress: A Computational Biology Approach

In recent times, Bio-informatics has become a very significant topic. Background research demonstrates that Anxiety, Bipolar Disorder, Heart disease, and Stress share a significant number of biochemical and genetic characteristics. Consequently, we need to find out the genetic interactions between these four diseases. The goal of the research is to identify analyses with the assistance of bio-informatics tools like Cytoscape, Gene-mania, NetworkAnalyst, and so on. Collecting genes from the NCBI database afterward filtering and discovering the gene association for situated above expressed diseases. Around here some fundamental steps such as pre-processing, filtering, and connecting of genes were made possible through the utilization of the Python programming language. These analyses have been successfully able to scrutinize numerous common genes where the number of associated genes identified for Anxiety, Bipolar Disorder (BD), heart disease, and Stress are 274, 768, 269, and 269 respectively. Afterward, we have to identify the top ten most significant hub genes based on the degree value of genes. In this study, Protein-Protein Interaction (PPI) networks, Protein Drug Interaction (PDI) networks, Functional Association Networks, protein-chemical interaction networks (PCI), and Gene interaction disease networks have been investigated aiming to originate an important suggestion for designing medications for some specific diseases. For this research, an efficient drug design is made attainable by how proteins bind to drug molecules.