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Fractal Dimension of Protein–Protein Interactions: A Tool for Cancer Protein Identification


Affiliations
1 Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi 682 039, India

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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  • Fractal Dimension of Protein–Protein Interactions: A Tool for Cancer Protein Identification

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Authors

Sminu Izudheen
Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi 682 039, India
Bee Fateema T. Shiras
Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi 682 039, India
R. Anamika
Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi 682 039, India
Annie Jaimy
Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi 682 039, India
R. Anna
Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi 682 039, India

Abstract


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.



DOI: https://doi.org/10.18520/cs%2Fv126%2Fi12%2F1454-1463