HIV1-Human Protein-Protein Interaction Prediction (HHPPIP) Methodology: An FP-Growth Based Association Rule Mining Approach

Title:HIV1-Human Protein-Protein Interaction Prediction (HHPPIP) Methodology: An FP-Growth Based Association Rule Mining Approach

Volume: 10 Issue: 4

Author(s): R. Geetha Ramani and Shomona Gracia Jacob

Affiliation:

Keywords: AIDS, association rule mining, data mining, FP-growth algorithm, HIV1-human PPI.

Abstract: Interactions between human and viral proteins have proved to be the major cause of several critical ailments and research directions in the recent past have focused on predicting potential viralhost protein relations through computational techniques. This research aimed at detecting probable interactions between HIV-1 and human proteins by generating all possible high -confident (>80%) associations between the host and viral protein and utilizing the association rules to predict new interactions. The FP-Growth algorithm was analyzed and found to evolve the exhaustive set of high confidence association rules that were mined further to isolate probable and significant HIV1-Human predictions with improved accuracy, sensitivity and specificity compared to previous work. The identified HIV1-Human protein interactions were further investigated using Gene Ontology based and DAVID functional annotation tool to establish their biological and therapeutic merits. The superiority of the proposed approach to previously applied computational techniques has been discussed. AIDS is one of the most dreaded diseases and we believe the proposed approach and the predicted interactions would be instrumental in expediting biological and molecular researchers towards formulating drugs for AIDS therapy and the biological functionality of the predicted interactions would enable timely diagnosis of the presence of the infectious viral protein and its replication in the host.

Cite this article as:

Geetha Ramani R. and Jacob Gracia Shomona, HIV1-Human Protein-Protein Interaction Prediction (HHPPIP) Methodology: An FP-Growth Based Association Rule Mining Approach, Current Bioinformatics 2015; 10 (4) . https://dx.doi.org/10.2174/157489361004150922150233