ArticlePloS one2012
A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Exploring psychological symptom associations among people living with HIV using an apriori algorithm.BMC infectious diseases · 2026Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
- Fuzzy association analysis for identifying climatic and socio-demographic factors impacting the spread of COVID-19.Methods (San Diego, Calif.) · 2022Article
- In silico prediction of HIV-1-host molecular interactions and their directionality.PLoS computational biology · 2022Article
- Biclustering-based association rule mining approach for predicting cancer-associated protein interactions.IET systems biology · 2019Article
- Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions.Briefings in functional genomics · 2018Review
- Preservation affinity in consensus modules among stages of HIV-1 progression.BMC bioinformatics · 2017Article
- Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways.Scientific reports · 2016Article
- A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs.BMC bioinformatics · 2016Article
- BiC2PAM: constraint-guided biclustering for biological data analysis with domain knowledge.Algorithms for molecular biology : AMB · 2016Article
- BicNET: Flexible module discovery in large-scale biological networks using biclustering.Algorithms for molecular biology : AMB · 2016Article
- Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.Journal of biosciences · 2015Article
- A primer to frequent itemset mining for bioinformatics.Briefings in bioinformatics · 2015Review
- A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.Scientific reports · 2015Article
- Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.PloS one · 2015Article
- Computational approaches for prediction of pathogen-host protein-protein interactions.Frontiers in microbiology · 2015Review
- Computational reconstruction of proteome-wide protein interaction networks between HTLV retroviruses and Homo sapiens.BMC bioinformatics · 2014Article
- Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach.BMC bioinformatics · 2014Article
- AdaBoost based multi-instance transfer learning for predicting proteome-wide interactions between Salmonella and human proteins.PloS one · 2014Article
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3 authors.
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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.
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