PREDICTION OF PROTEIN INTERACTIONS ON HIV-1–HUMAN PPI DATA USING A NOVEL CLOSURE-BASED INTEGRATED APPROACH
Kartick Chandra Mondal, Nicolas Pasquier, Anirban Mukhopadhyay, Célia da Costa Pereira, Ujjwal Maulik, Andrea G. B. Tettamanzi · 2012
Discovering Protein-Protein Interactions (PPI) is a new interesting challenge in computational biology.Identifying interactions among proteins was shown to be useful for finding new drugs and preventing several kinds of diseases.The identification of interactions between HIV-1 proteins and Human proteins is a particular PPI problem whose study might lead to the discovery of drugs and important interactions responsible for AIDS.We present the FIST algorithm for extracting hierarchical bi-clusters and minimal covers of association rules in one process.This algorithm is based on the frequent closed itemsets framework to efficiently generate a hierarchy of conceptual clusters and non-redundant sets of association rules with supporting object lists.Experiments conducted on a HIV-1 and Human proteins interaction dataset show that the approach efficiently identifies interactions previously predicted in the literature and can be used to predict new interactions based on previous biological knowledge.