A Weighted Association Rule Mining Method for Predicting HCV-Human Protein Interactions

Background: Hepatitis C Virus causes the most severe form of chronic liver disease and nearly 200 million people worldwide are estimated to be infected with this virus. Much about the HCV pathogenesis process is still unknown. The study of interactions between HCV and human proteins will lead to deeper understanding of HCV mechanism. Objective: The objective of this paper is to predict potentially new HCV-Human protein interactions using a weighted association rule mining technique. Methods: A new computational method was developed for mining associations within a bipartite graph that was constructed from the HCV-human protein interactions dataset. A new mathematical model was applied to weigh the discovered association rules based on Gene Ontology annotations of viral and human proteins. HCVpro database was used to generate a human-viral bipartite graph that was then analyzed computationally to extract biclusters within the graph. Association rules were extracted from the bipartite graph and weighted using a mathematical model that incorporated information about proteins available from Gene Ontology knowledge base. Results: Forty two new interactions between HCV and human proteins were predicted. Some of these predicted interactions were validated through literature survey and enrichment studies such as Gene ontology-based analysis, pathway-based analysis and disease association based analysis. Conclusion: The methodology developed in this paper can also be used for various other kind of data analysis and hence it carries a wide scope. This will be useful to conduct similar kind of experiments for other disease databases.