A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-3: association rule mining-based approaches <s> BackgroundIn the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes.ResultsWe developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence.ConclusionsUnderstanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention. <s> BIB001 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Category-3: association rule mining-based approaches <s> Association rule mining and bi-clustering are data mining tasks that have become very popular in many application domains, particularly in bioinformatics. However, to our knowledge, no algorithm was introduced for performing these two tasks in one process. We propose a new approach called FIST for extracting bases of extended association rules and conceptual bi-clusters conjointly. This approach is based on the frequent closed itemsets framework and requires a unique scan of the database. It uses a new suffix tree based data structure to reduce memory usage and improve the extraction efficiency, allowing parallel processing of the tree branches. Experiments conducted to assess its applicability to very large datasets show that FIST memory requirements and execution times are in most cases equivalent to frequent closed itemsets based algorithms and lower than frequent itemsets based algorithms. <s> BIB002
In Mukhopadhyay et al.  , an attempt was made to predict interactions based on association rule mining technique. Here the well-known Apriori  algorithm is used for generating association rules between human proteins interacting with HIV-1 proteins. Some novel interactions are then predicted using those rules. Besides the prediction task, the discovered rules describe an association between different human proteins in the context of HIV-1 interaction. The main advantage of using the association rule mining BIB002 technique for prediction purpose, is that it does not need any negative samples for prediction unlike classification-based approaches, where predictions are highly BIB001 Protein structure information is integrated for prediction of PPIs. The main idea behind this technique is that given a set of proteins with defined structures and associated interactions, proteins with similar structures or substructures with them will tend to share the same interaction partners. An association rule mining technique based on biclustering is used for predicting PPIs. Predictions are defined from the association rules that are generated using a novel biclustering-based association rule mining technique.