Abstract:
Background: By incorporating computer science concepts, bioinformatics is a vital tool in the field of genomics that helps to comprehend complex biological processes. This multidisciplinary discipline uses statistical analysis, data processing, and computer tools to mine biological data for useful insights. Because bioinformatics integrates techniques from several fields, it is essential in many areas, including as genetics, education, and healthcare. Aim: This study's main objective is to understand the intricate interaction between genetic variants and IPF, COPD, and LC. We aim to investigate the connections between various respiratory disorders by identifying similar pathways, and looking at TF-gene interactions, Gene-miRNA interactions, and Protein-Drug interactions. Methodology: We carried out a comprehensive analysis of the PPI and PDI networks for IPF, COPD, and LC using a computational technique. Associated genes were taken out of the Finding genes involved using the NCBI gene database and a data mining method that were shared by the three diseases of the lungs. Result: CFH, ETS1, CCNL1, NEDD9, MSH2, RORA, PMAIP1, SORD, genes shared by IPF, COPD, and lung cancer were identified by the computational model. Through the continued unraveling of a novel pathway, the study shed light on various respiratory disorders as well as possible molecular indicators. Conclusion: The PPI and PDI networks that were built using the shared genes that were found provide important new information about the molecular makeup of lung cancer, COPD, and IPF. This knowledge is important for medication design as well as for understanding the complex ligand binding that occurs inside the PPI network. Our research advances our knowledge of the intricate molecular mechanisms behind various respiratory illnesses by identifying important pathways and molecular biomarker
