title: A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions.
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.
authors.forename: Sanghamitra
authors.initials: S
authors.lastname: Bandyopadhyay
authors.forename: Sumanta
authors.initials: S
authors.lastname: Ray
authors.forename: Anirban
authors.initials: A
authors.lastname: Mukhopadhyay
authors.forename: Ujjwal
authors.initials: U
authors.lastname: Maulik
mesh_terms: D003198:Computer Simulation; D015497:HIV-1 / Q000378:metabolism*; D006801:Humans; D011485:Protein Binding; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins
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