Integrasi data Protein-Protein Interactions dan Pathway untuk Menentukan Score pada pathway Menggunakan Analisis Graf - Lailan Sahrina Hasibuan; Ahmad Fariqi; Lilik Prayitno; Melly Br Bangun (STMIK Budi Darma, 2023)
The development of molecular biology technology produces large amounts of omics data. Integration of omics data is useful for the analysis of biological processes at the molecular level, such as protein expression, drug mechanisms against diseases, and mechanisms of inheritance. This study aims to integrate protein molecular biology data through protein-protein interactions (PPIs), pathways, modules and orthology, to calculate pathway scores. The score calculation uses the degree calculation on the graph concept. Proteins, pathways, modules and orthologs act as nodes, while the interactions between them act as edges. Furthermore, according to the concept of a graph, nodes with a high degree represent nodes that have an important role in a graph. Based on this concept, the most important pathway related to a protein is the pathway with the highest degree in a multipartite graph formed by PPIs, modules, orthologs and pathways. The output of this study is a package in the R language to integrate data on molecular biology of proteins, pathways, modules and orthology, then displays the pathways that have the most role in protein based on the order of the highest score. This package was tested using protein Insulin (INS) and Xanthine dehydrogenase (XDH) inputs. The results of calculating the score on the pathway for INS produced the pathway with the highest score, namely MAPK signaling pathway (0.18) lane 1, Pathways in cancer (0.137) lane 2, Ubiquitin mediated proteolysis (0.28) lane 3. XDH protein input produces Purine metabolism pathway (0.67) lane 1, Metabolic pathways (0.48) lane 2 and Purine metabolism (0.23) lane 3. These results can be used for enrichment analysis regarding the relationship between proteins and pathways.