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

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

Journal Name: Current Bioinformatics

Volume 10 , Issue 4 , 2015

Become EABM
Become Reviewer
Call for Editor

Graphical 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.

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

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2015
Published on: 22 September, 2015
Page: [441 - 455]
Pages: 15
DOI: 10.2174/157489361004150922150233
Price: $65

Article Metrics

PDF: 17
PRC: 1