Graph theoretical way of understanding protein-protein interaction in ovarian cancer

Cancer is a major research area in the medical field. Precise assessment of non-similar cancer types holds great significance in according to better treatment and reducing the risk of destructiveness in patients’ health. Cancer comprises a ambient that differs in response to therapy, signaling mechanisms, cytology and physiology. Netting theory and graph theory jointly gives a viable way to probe the proteomic specific data of cancer types such as ovarian, colon, breast, oral, cervical, prostate, and lung. We observe that the P2P(protein-protein) interaction Nettings of the cancerous tissues blended with the seven cancers and normal have same structural attributes. But some of these point to desultory changes from the disease Nettings to normal implying the variation in the dealings and bring out the redoing in the complicacy of various cancers. The Netting-based approach has a pertinent role in precision oncology. Cancer can be better dealt with through mutated pathways or Nettings in preference to individual mutations and that the utility value of repositioned drugs can be understood from disease modules in molecular Nettings. In this paper, we demonstrate how the graph theory and neural Nettings act as vital tools for understanding cancer and other types such as ovarian cancer at the zeroth level.