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Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach

BACKGROUND: Discovering 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...

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Autores principales: Mukhopadhyay, Anirban, Ray, Sumanta, Maulik, Ujjwal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3922888/
https://www.ncbi.nlm.nih.gov/pubmed/24460683
http://dx.doi.org/10.1186/1471-2105-15-26
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author Mukhopadhyay, Anirban
Ray, Sumanta
Maulik, Ujjwal
author_facet Mukhopadhyay, Anirban
Ray, Sumanta
Maulik, Ujjwal
author_sort Mukhopadhyay, Anirban
collection PubMed
description BACKGROUND: Discovering 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. RESULTS: Here 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. CONCLUSIONS: We 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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spelling pubmed-39228882014-02-28 Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach Mukhopadhyay, Anirban Ray, Sumanta Maulik, Ujjwal BMC Bioinformatics Methodology Article BACKGROUND: Discovering 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. RESULTS: Here 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. CONCLUSIONS: We 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. BioMed Central 2014-01-24 /pmc/articles/PMC3922888/ /pubmed/24460683 http://dx.doi.org/10.1186/1471-2105-15-26 Text en Copyright © 2014 Mukhopadhyay et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Mukhopadhyay, Anirban
Ray, Sumanta
Maulik, Ujjwal
Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach
title Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach
title_full Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach
title_fullStr Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach
title_full_unstemmed Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach
title_short Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach
title_sort incorporating the type and direction information in predicting novel regulatory interactions between hiv-1 and human proteins using a biclustering approach
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3922888/
https://www.ncbi.nlm.nih.gov/pubmed/24460683
http://dx.doi.org/10.1186/1471-2105-15-26
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