<![CDATA[One of the metabolic diseases with a rising prevalence in Indonesia is Type 2 Diabetes Mellitus (T2DM). A collective effort from various sectors is required to seek solutions for T2DM. The proteomic approach, which focuses on proteins and their interactions related to T2DM, can be used to understand this condition. This research aims to model protein interactions associated with T2DM using a network graph, enabling the identification of key proteins that have the potential to serve as therapeutic targets or T2DM biomarkers. The graph analysis method used in this study involved four centrality measures: degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The validation method used to confirm the identified proteins is gene set enrichment analysis. The results obtained from the graph analysis using four centrality measures highlighted that seven out of 27 T2DM-related proteins are key proteins; these are: ABCC8, HNF4A, INS, KCNJ11, NEUROD1, PDX1, and SLC30A8. This study concludes that graph analysis on the interaction graph of T2DM-related proteins successfully identified key proteins that could potentially serve as T2DM biomarkers. Further medical investigation is imperative because computational identification alone is not sufficient to confirm the validity of the findings in this study.]]>
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