Predicting annotated HIV-1-Human PPIs using a biclustering approach to association rule mining

Discovering novel interactions between HIV-1 and human proteins would greatly contribute to the areas of HIV research. Identification of such interactions leads to a greater insight into drug target prediction. Here we have proposed an association rule mining technique based on biclustering for identifying a set of rules among the human proteins as well as HIV-1 proteins and using those rules some novel interactions are predicted. For prediction both the interaction types and direction of regulation of the interactions, are considered to provide accessible insight into HIV-1 infection. We have studied the biclusters and analyzed the significant GO terms and pathways where the human proteins of the biclusters participate. The predicted rules are further analyzed to discover regulatory relationships between some human proteins in course of HIV-1 infection. Some experimental evidences are collected from recent literature for validating the predicted interactions.