ArticleBMC bioinformatics2014
Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach.Scientific reports · 2017Article
- A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs.BMC bioinformatics · 2016Article
- BicNET: Flexible module discovery in large-scale biological networks using biclustering.Algorithms for molecular biology : AMB · 2016Article
- Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways.BMC bioinformatics · 2015Article
- A simple feature construction method for predicting upstream/downstream signal flow in human protein-protein interaction networks.Scientific reports · 2015Article
- Computational approaches for prediction of pathogen-host protein-protein interactions.Frontiers in microbiology · 2015Review
- HIV-1, human interaction database: current status and new features.Nucleic acids research · 2015Article
- Network-based study reveals potential infection pathways of hepatitis-C leading to various diseases.PloS one · 2014Article
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3 authors.
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Abstract
backgroundDiscovering novel interactions between HIV-1 and human proteins would greatly contribute to different areas of HIV research. Identification of such interactions leads to a greater insight into drug target prediction. Some recent studies have been conducted for computational prediction of new interactions based on the experimentally validated information stored in a HIV-1-human protein-protein interaction database. However, these techniques do not predict any regulatory mechanism between HIV-1 and human proteins by considering interaction types and direction of regulation of interactions.
resultsHere we present an association rule mining technique based on biclustering for discovering a set of rules among human and HIV-1 proteins using the publicly available HIV-1-human PPI database. These rules are subsequently utilized to predict some novel interactions among HIV-1 and human proteins. For prediction purpose both the interaction types and direction of regulation of interactions, (i.e., virus-to-host or host-to-virus) are considered here to provide important additional information about the regulation pattern of interactions. We have also studied the biclusters and analyzed the significant GO terms and KEGG pathways in which the human proteins of the biclusters participate. Moreover the predicted rules have also been analyzed to discover regulatory relationship between some human proteins in course of HIV-1 infection. Some experimental evidences of our predicted interactions have been found by searching the recent literatures in PUBMED. We have also highlighted some human proteins that are likely to act against the HIV-1 attack.
conclusionsWe pose the problem of identifying new regulatory interactions between HIV-1 and human proteins based on the existing PPI database as an association rule mining problem based on biclustering algorithm. We discover some novel regulatory interactions between HIV-1 and human proteins. Significant number of predicted interactions has been found to be supported by recent literature.
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