Functional association between influenza A (H1N1) virus and human

Huang, Tao; Cui, Wenjin; He, Zhisong; Hu, Le-Le; Liu, Fang; Wen, Tieqiao; Li, Yixue; Cai, Yu-Dong

doi:10.1016/j.bbrc.2009.08.131

Cited by 18 publications

(10 citation statements)

References 15 publications

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“…Based on the ranked genes according to their importance after mRMR evaluation, we used the Incremental Feature Selection (IFS) (see, e.g., [1], [47]) to determine the optimal number of genes as biomarkers. During the IFS procedure, genes in the ranked gene set are added one by one from higher to lower rank.…”

Section: Methodsmentioning

confidence: 99%

Identification of Colorectal Cancer Related Genes with mRMR and Shortest Path in Protein-Protein Interaction Network

et al. 2012

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One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING ( S earch T ool for the R etrieval of I nteracting G enes), a weighted functional protein association network was constructed. By means of the mRMR ( M aximum R elevance M inimum R edundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.

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Section: Methodsmentioning

confidence: 99%

Identification of Colorectal Cancer Related Genes with mRMR and Shortest Path in Protein-Protein Interaction Network

et al. 2012

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“…Network analysis is a powerful tool for identifying phenotype-related changes and potentially predicting disease outcome (Chuang et al, 2007;Huang et al, 2009aHuang et al, , 2010aHuang et al, , 2011aHuang et al, , 2011cTaylor et al, 2009). The network can provide a more comprehensive and robust characterization of aging than physiological variables or gene expression alone.…”

Section: Introductionmentioning

confidence: 99%

Crosstissue Coexpression Network of Aging

Huang

Zhang

Xie

et al. 2011

OMICS: A Journal of Integrative Biology

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Aging is characterized by the interlocking decay of biological functions over time. Microarrays have been successful in elucidating some of the genome-wide changes that occur with age. Using the AGEMAP dataset that catalogs changes in gene expression as a function of age in 16 tissues in mice, we identified tissue-specific aging genes. Coordinated aging processes across different tissues then were clarified in crosstissue coexpression networks on both the gene and pathway levels. Our findings provide more concrete information about coordinated aging across different tissues. By bridging gene-level and tissue-level research, this study could help identify targets for attenuation of critical aging-related genes, pathways, or networks for antiaging intervention.

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“…The concept of infection information exchanger genes is useful for revealing key players in virus infection and for providing candidates for drug target screening. It can be easily applied to other virus-related studies, such as hepatitis B virus (HBV), hepatitis C virus (HCV) [29], and influenza A (H1N1) virus [10].…”

Section: Discussionmentioning

confidence: 99%

“…Microarray technology makes it possible to measure the expression of thousands of genes. Information on protein interactions can help us understand the mechanisms of biological problems [8], [9], [10]. The combination of these technologies may allow us to elucidate the mechanisms of HIV-1 infection and resistance.…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis of HIV-1 Resistance Based on Gene Expression Profiles and the Virus-Host Interaction Network

Huang

Lei

et al. 2011

PLoS ONE

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A very small proportion of people remain negative for HIV infection after repeated HIV-1 viral exposure, which is called HIV-1 resistance. Understanding the mechanism of HIV-1 resistance is important for the development of HIV-1 vaccines and Acquired Immune Deficiency Syndrome (AIDS) therapies. In this study, we analyzed the gene expression profiles of CD4+ T cells from HIV-1-resistant individuals and HIV-susceptible individuals. One hundred eighty-five discriminative HIV-1 resistance genes were identified using the Minimum Redundancy-Maximum Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The virus protein target enrichment analysis of the 185 HIV-1 resistance genes suggested that the HIV-1 protein nef might play an important role in HIV-1 infection. Moreover, we identified 29 infection information exchanger genes from the 185 HIV-1 resistance genes based on a virus-host interaction network analysis. The infection information exchanger genes are located on the shortest paths between virus-targeted proteins and are important for the coordination of virus infection. These proteins may be useful targets for AIDS prevention or therapy, as intervention in these pathways could disrupt communication with virus-targeted proteins and HIV-1 infection.

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Functional association between influenza A (H1N1) virus and human

Cited by 18 publications

References 15 publications

Identification of Colorectal Cancer Related Genes with mRMR and Shortest Path in Protein-Protein Interaction Network

Identification of Colorectal Cancer Related Genes with mRMR and Shortest Path in Protein-Protein Interaction Network

Crosstissue Coexpression Network of Aging

Computational Analysis of HIV-1 Resistance Based on Gene Expression Profiles and the Virus-Host Interaction Network

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