A Computational Study Identifies HIV Progression-Related Genes Using mRMR and Shortest Path Tracing

Dong, Xiao; Li, Rudong; Liu, Lei

doi:10.1371/journal.pone.0078057

Cited by 10 publications

(12 citation statements)

References 43 publications

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“…The major steps of mRMR implementation were the same as we previously described [13]. The algorithm aimed to balance features’ relevance to the prediction target and the redundancy between features.…”

Section: Methodsmentioning

confidence: 99%

“…To address this issue, we herein adopt a two-step pipeline widely-used in previous studies, which includes machine learning to identify disease-related genes and pathway analysis to reveal molecular interactions among the genes [ 13 - 16 ]. First, we utilize the machine learning strategy for accurate classification of prostate cancer phenotypes based on gene expression microarray data.…”

Section: Introductionmentioning

confidence: 99%

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Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis

Dong

Liu

2014

Theor Biol Med Model

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BackgroundProstate cancer is one of the most common malignant diseases and is characterized by heterogeneity in the clinical course. To date, there are no efficient morphologic features or genomic biomarkers that can characterize the phenotypes of the cancer, especially with regard to metastasis – the most adverse outcome. Searching for effective surrogate genes out of large quantities of gene expression data is a key to cancer phenotyping and/or understanding molecular mechanisms underlying prostate cancer development.ResultsUsing the maximum relevance minimum redundancy (mRMR) method on microarray data from normal tissues, primary tumors and metastatic tumors, we identifed four genes that can optimally classify samples of different prostate cancer phases. Moreover, we constructed a molecular interaction network with existing bioinformatic resources and co-identifed eight genes on the shortest-paths among the mRMR-identified genes, which are potential co-acting factors of prostate cancer. Functional analyses show that molecular functions involved in cell communication, hormone-receptor mediated signaling, and transcription regulation play important roles in the development of prostate cancer.ConclusionWe conclude that the surrogate genes we have selected compose an effective classifier of prostate cancer phases, which corresponds to a minimum characterization of cancer phenotypes on the molecular level. Along with their molecular interaction partners, it is fairly to assume that these genes may have important roles in prostate cancer development; particularly, the un-reported genes may bring new insights for the understanding of the molecular mechanisms. Thus our results may serve as a candidate gene set for further functional studies.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis

Dong

Liu

2014

Theor Biol Med Model

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“…The betweenness centrality quantifies the number of times a protein acts as a bridge along the shortest path between two other proteins in the network (Figure 3B). The degree and centrality measures can be used to identify the essential human proteins influencing HIV-human interactions (Dickerson et al, 2010;van Dijk et al, 2010;Huang et al, 2011;Li et al, 2013;Ma et al, 2013;Bandyopadhyay et al, 2015;Xie et al, 2015). For example, Huang et al (2011) compared gene expression profiles from CD4+ T cells between HIV-1-resistant and susceptible subjects using Minimum Redundancy-Maximum Relevance and Incremental Feature Selection algorithms.…”

Section: Identification Of Essential Proteins In a Protein-protein Inmentioning

confidence: 99%

Network-Based Analysis of OMICs Data to Understand the HIV–Host Interaction

et al. 2020

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The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding networkbased analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

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“…The betweenness centrality quantifies the number of times a protein acts as a bridge along the shortest path between two other proteins in the network. The degree and centrality measures can be used to identify the essential human proteins influencing HIV-human interactions (Dickerson et al, 2010;van Dijk et al, 2010;Huang et al, 2011;Li et al, 2013;Ma et al, 2013;Bandyopadhyay et al, 2015;Xie et al, 2015). For example, Huang T. and colleagues compared gene expression profiles from CD4+ T cells between HIV-1-resistant and susceptible subjects using Minimum Redundancy-Maximum Relevance and Incremental Feature Selection algorithms.…”

Section: Identification Of Essential Proteins In a Protein-protein Inmentioning

confidence: 99%

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

Ivanov

Lagunin

Filimonov

et al. 2020

Preprint

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The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-1-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies may represent a basis for developing new anti-HIV therapeutic strategies such as new small-molecule drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

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A Computational Study Identifies HIV Progression-Related Genes Using mRMR and Shortest Path Tracing

Cited by 10 publications

References 43 publications

Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis

Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis

Network-Based Analysis of OMICs Data to Understand the HIV–Host Interaction

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

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