A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

Abstract

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed.

A structured view on pattern mining-based biclustering

“…Bellay's et al method [14] uses the Apriori miner [2] with additional principles to evaluate the functional coherency of the discovered biclusters against the background noise. This is one of diverse PM-based attempts to exhaustively discover dense biclusters in either unweighted networks [13,90,133,80] or, more interestingly, in scored networks [32,30]. GenMiner [86] includes external knowledge within the input matrix to derive biclusters from association rules that relate annotations (external grouping of rows or columns) with clusters derived from (closed) frequent patterns using CLOSE [102].…”

Protein–Protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM

“…We would also like to improve the generalization ability of the classifier in the hyperspace (Chen and Wang 2003;Chiang and Hao 2004;Ishibuchi and Yamamoto 2005). Association rule mining may be useful to define more possibility of interaction (Mukhopadhyay et al 2012) and we may also try to use the idea of bi-clusters on the PPI database (Maulik et al 2011a, b). To achieve such an objective, Brainstorming consensus (Plewczynski 2010) or weighted Markov chain-based rank aggregation approach (Sengupta et al 2012) may also be used for further improvement.…”

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

“…An itemset is called closed itemset if none of its proper supersets have the same support value. Finding the set of frequent itemsets is equivalent to find a set of all-1 biclusters each having at least min sup number of rows [7]. BiMax generates all maximal biclusters and as the columns of maximal biclusters represent a closed itemset, so all extracted biclusters satisfying min sup condition provide the set of frequent closed itemsets.…”

“…A similar biclustering approach is studied in [6] to find immunodeficiency gateway proteins and their involvement in microRNA regulation. In another study [7], an association rule mining approach is proposed for finding a set of association rules from PPI data and these rules are used for predicting new interactions. In both the studies [6], [7], the interaction types and regulation direction are not considered in finding the bicliques.…”