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Fractal Dimension of Protein–Protein Interactions: A Tool for Cancer Protein Identification
Early cancer diagnosis is critical as it can help avoid the risks associated with long-term treatments and even prevent death. Identifying a defining trait of a cancer protein within its protein–protein interaction (PPI) network could lead to a significant breakthrough in accelerating early cancer detection. A systematic analysis of various topological properties of cancer proteins in the PPI network, focused on their fractal dimension, was conducted. It was observed that cancer proteins exhibit a high fractal dimension (with an average of 1.21). Those with the highest fractal dimension play a significant role in multiple mutation pathways. The observation that TP53 protein occupying high fractal dimension of 1.68, connected with 48 communities reaffirm the correctness of the approach. Further research in this area will provide valuable insights into the structural and functional complexity of cellular processes regulated by proteins, leading to the development of robust therapeutic approaches and enhancing our understanding of cancer biology.
Keywords
Betweenness centrality, cancer gene identification, degree centrality, eigenvector centrality, protein interaction network.
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