Relaxed Neighbor Based Graph Transformations for Effective Preprocessing: A Function Prediction Case Study

Abstract

Protein-protein interaction (PPI) networks are valuable biological source of data which contain rich information useful for protein function prediction. The PPI networks face data quality challenges like noise in the form of false positive edges and incompleteness in the form of missing biologically valued edges. These issues can be handled by enhancing data quality through graph transformations for improved protein function prediction. We proposed an improved method to extract similar proteins based on the notion of relaxed neighborhood. The proposed method can be applied to carry out graph transformation of PPI network datasets to improve the performance of protein function prediction task by adding biologically important protein interactions, removing dissimilar interactions and increasing reliability score of the interactions. By preprocessing PPI network datasets with the proposed methodology, the experiments conducted on both un-weighted and weighted PPI network datasets show that, the proposed methodology enhances the data quality and improves prediction accuracy over other approaches. The results indicate that the proposed approach could utilize underutilized knowledge, such as distant relationships embedded in the PPI graph.