Name
Abstract | ||
|---|---|---|
One of the most challenging problems in biomedicine and genomics is the identification of disease biomarkers. In this study, proteomics data from seven major cancers were used to construct two weighted protein-protein interaction networks, i.e., one for the normal and another for the cancer conditions. We developed rigorous, yet mathematically simple, methodology based on the degeneracy at -1 eigenvalues to identify structural symmetry or motif structures in network. Utilizing eigenvectors corresponding to degenerate eigenvalues in the weighted adjacency matrix, we identified structural symmetry in underlying weighted protein-protein interaction networks constructed using seven cancer data. Functional assessment of proteins forming these structural symmetry exhibited the property of cancer hallmarks. Survival analysis refined further this protein list proposing BMI, MAPK11, DDIT4, CDKN2A, and FYN as putative multicancer biomarkers. The combined framework of networks and spectral graph theory developed here can be applied to identify symmetrical patterns in other disease networks to predict proteins as potential disease biomarkers. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1017/nws.2019.55 | NETWORK SCIENCE |
Keywords | Field | DocType |
cancer networks, eigenvalue analysis, graph symmetry, biomarkers | Cognitive science,Biomarker (medicine),Artificial intelligence,Cancer,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
7 | 4 | 2050-1242 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pramod Shinde | 1 | 0 | 0.34 |
| Loïc Marrec | 2 | 0 | 0.34 |
| Aparna Rai | 3 | 0 | 0.34 |
| Alok Yadav | 4 | 0 | 0.34 |
| R. Raju | 5 | 8 | 7.91 |
| Mikhail Ivanchenko | 6 | 0 | 0.34 |
| Alexey Zaikin | 7 | 2 | 2.12 |
| Sarika Jalan | 8 | 7 | 5.18 |