Literature DB >> 25479794

A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions.

Sanghamitra Bandyopadhyay, Sumanta Ray, Anirban Mukhopadhyay, Ujjwal Maulik.   

Abstract

The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community.
© The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  HIV dependency factor; HIV-1-human PPI Network; association rule mining; biclustering; computational PPI prediction; random forest classifier; rank aggregation; semi supervised classification; topological properties of network

Mesh:

Substances:

Year:  2014        PMID: 25479794     DOI: 10.1093/bib/bbu041

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  11 in total

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2.  Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways.

Authors:  Suyu Mei; Kun Zhang
Journal:  Sci Rep       Date:  2016-07-29       Impact factor: 4.379

3.  Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate.

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4.  A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer's disease progression.

Authors:  Sumanta Ray; Sk Md Mosaddek Hossain; Lutfunnesa Khatun; Anirban Mukhopadhyay
Journal:  BMC Bioinformatics       Date:  2017-12-20       Impact factor: 3.169

5.  Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach.

Authors:  Sumanta Ray; Ujjwal Maulik
Journal:  Sci Rep       Date:  2017-03-07       Impact factor: 4.379

6.  Preservation affinity in consensus modules among stages of HIV-1 progression.

Authors:  Sk Md Mosaddek Hossain; Sumanta Ray; Anirban Mukhopadhyay
Journal:  BMC Bioinformatics       Date:  2017-03-20       Impact factor: 3.169

7.  Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model.

Authors:  Zhirui Fan; Wenhua Xue; Lifeng Li; Chaoqi Zhang; Jingli Lu; Yunkai Zhai; Zhenhe Suo; Jie Zhao
Journal:  J Transl Med       Date:  2018-07-20       Impact factor: 5.531

8.  Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis.

Authors:  Suyu Mei; Erik K Flemington; Kun Zhang
Journal:  BMC Genomics       Date:  2018-06-28       Impact factor: 3.969

9.  A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs.

Authors:  Sumanta Ray; Sanghamitra Bandyopadhyay
Journal:  BMC Bioinformatics       Date:  2016-03-08       Impact factor: 3.169

Review 10.  Computational analysis of protein interaction networks for infectious diseases.

Authors:  Archana Pan; Chandrajit Lahiri; Anjana Rajendiran; Buvaneswari Shanmugham
Journal:  Brief Bioinform       Date:  2015-08-10       Impact factor: 11.622

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