Randomness and preserved patterns in cancer network

Abstract

Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.

21 pages, 9 figures

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Cited by in corpus (8)

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• Spectral statistics of random geometric graphs
• Random matrix analysis for gene interaction networks in cancer cells
• Spontaneous back-pain alters randomness in functional connections in large scale brain networks: A random matrix perspective
• Ordered Level Spacing Distribution in Embedded Random Matrix Ensembles