Diffusion kernel to identify missing PPIs in protein network biomarker

Little focus has been placed on neighborhood proteins in the protein-protein interaction (PPI) network that do not physically interact with each other but have a higher likelihood to interact than the actual PPIs in the network. Identifying these missing PPIs would complete the protein network biomarker representing a disease. In the present study, we check the capability of diffusion kernel in identifying these missing PPIs. A diffusion kernel is a computational framework which is based on a physical phenomenon of gas diffusion and a computer science concept of random walk on a graph or network. We seek to predict probable missing PPIs related to Allergy and Asthma using diffusion kernel, by employing a threshold on the kernel values. The completed protein network biomarker can be used as a better predictor for disease identification and classification. This would also help in better understanding of disease mechanism at protein network level. Our results show that the network with high PPI score has better accuracy of predicting missing PPIs.