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Search Results: 1 - 10 of 303291 matches for " Vincent J Carey "
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Graphs in molecular biology
Huber Wolfgang,Carey Vincent J,Long Li,Falcon Seth
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-s6-s8
Abstract: Graph theoretical concepts are useful for the description and analysis of interactions and relationships in biological systems. We give a brief introduction into some of the concepts and their areas of application in molecular biology. We discuss software that is available through the Bioconductor project and present a simple example application to the integration of a protein-protein interaction and a co-expression network.
Machine Learning and Its Applications to Biology
Adi L Tarca,Vincent J Carey,Xue-wen Chen,Roberto Romero,Sorin Dr?ghici
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030116
Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes
Jen-hwa Chu, Ross Lazarus, Vincent J Carey, Benjamin A Raby
BMC Systems Biology , 2011, DOI: 10.1186/1752-0509-5-89
Abstract: Here we describe a novel approach for quantifying the differences in gene-gene connectivity patterns across disease states based on Graphical Gaussian Models (GGMs). We compare the posterior probabilities of connectivity for each gene pair across two disease states, expressed as a posterior odds-ratio (postOR) for each pair, which can be used to identify network components most relevant to disease status. The method can also be generalized to model differential gene connectivity patterns within previously defined gene sets, gene networks and pathways. We demonstrate that the GGM method reliably detects differences in network connectivity patterns in datasets of varying sample size. Applying this method to two independent breast cancer expression data sets, we identified numerous reproducible differences in network connectivity across histological grades of breast cancer, including several published gene sets and pathways. Most notably, our model identified two gene hubs (MMP12 and CXCL13) that each exhibited differential connectivity to more than 30 transcripts in both datasets. Both genes have been previously implicated in breast cancer pathobiology, but themselves are not differentially expressed by histologic grade in either dataset, and would thus have not been identified using traditional differential gene expression testing approaches. In addition, 16 curated gene sets demonstrated significant differential connectivity in both data sets, including the matrix metalloproteinases, PPAR alpha sequence targets, and the PUFA synthesis pathway.Our results suggest that GGM can be used to formally evaluate differences in global interactome connectivity across disease states, and can serve as a powerful tool for exploring the molecular events that contribute to disease at a systems level.Network and pathway models have been frequently used to describe complex interaction patterns of genes and other types of molecules, and there is increasing recognition that such networ
A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism
Jen-hwa Chu, Scott T Weiss, Vincent J Carey, Benjamin A Raby
BMC Systems Biology , 2009, DOI: 10.1186/1752-0509-3-55
Abstract: Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3) identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4) evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2) and Interleukin 1B (IL1B), two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics.Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.Jansen and Nap [1] proposed expression quantitative trait locus (eQTL) mapping by considering gene transcript abundances as quantitative phenotypes. Identified eQTLs could then be tested as potential disease-susceptibility candidates in genetic association studies, with the expectation that variants with functional influence on gene expression would have a higher likelihood of influencing clinical traits. Initial studies examining the feasibility of such integrative genomic strategies in a variety of model organisms and in human populations have demonstrated that a substantial proportion of transcripts exhibit heritable expressio
Four Papers on Contemporary Software Design Strategies for Statistical Methodologists
Vincent Carey,Dianne Cook
Statistics , 2014, DOI: 10.1214/14-STS481
Abstract: Software design impacts much of statistical analysis and, as technology changes, dramatically so in recent years, it is exciting to learn how statistical software is adapting and changing. This leads to the collection of papers published here, written by John Chambers, Duncan Temple Lang, Michael Lawrence, Martin Morgan, Yihui Xie, Heike Hofmann and Xiaoyue Cheng.
Diarrhea as a cause of mortality in a mouse model of infectious colitis
Diana Borenshtein, Rebecca C Fry, Elizabeth B Groff, Prashant R Nambiar, Vincent J Carey, James G Fox, David B Schauer
Genome Biology , 2008, DOI: 10.1186/gb-2008-9-8-r122
Abstract: Computational analysis identified 462 probe sets more than 2-fold differentially expressed between uninoculated resistant and susceptible mice. In response to C. rodentium infection, 5,123 probe sets were differentially expressed in one or both lines of mice. Microarray data were validated by quantitative real-time RT-PCR for 35 selected genes and were found to have a 94% concordance rate. Transcripts represented by 1,547 probe sets were differentially expressed between susceptible and resistant mice regardless of infection status, a host effect. Genes associated with transport were over-represented to a greater extent than even immune response-related genes. Electrolyte analysis revealed reduction in serum levels of chloride and sodium in susceptible animals.The results support the hypothesis that mortality in C. rodentium-infected susceptible mice is associated with impaired intestinal ion transport and development of fatal fluid loss and dehydration. These studies contribute to our understanding of the pathogenesis of C. rodentium and suggest novel strategies for the prevention and treatment of diarrhea associated with intestinal bacterial infections.Acute diarrheal illness is one of the most important health problems in the world today, particularly in young children in developing countries. This life-threatening illness occurs in approximately four billion individuals per year and causes more than two million deaths worldwide each year [1]. The most common cause of diarrhea is gastrointestinal infection. Infection results in increased intestinal secretion and/or decreased intestinal absorption followed by fluid and electrolyte loss and dehydration that can be fatal if not treated [2,3]. Among the most important bacterial causes of diarrhea are enteropathogenic and enterohaemorrhagic Escherichia coli (EPEC and EHEC, respectively) [4]. These pathogens produce ultrastructural changes characterized by intimate bacterial adhesion to the apical surface of enterocytes
Software for Computing and Annotating Genomic Ranges
Michael Lawrence ,Wolfgang Huber,Hervé Pagès,Patrick Aboyoun,Marc Carlson,Robert Gentleman,Martin T. Morgan,Vincent J. Carey
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1003118
Abstract: We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization.
An Algorithm for Clustered Data Generalized Additive Modelling with S-PLUS
Lin Yee Hin,Vincent Carey
Journal of Statistical Software , 2005,
Expression analysis of asthma candidate genes during human and murine lung development
Erik Melén, Alvin T Kho, Sunita Sharma, Roger Gaedigk, J Steven Leeder, Thomas J Mariani, Vincent J Carey, Scott T Weiss, Kelan G Tantisira
Respiratory Research , 2011, DOI: 10.1186/1465-9921-12-86
Abstract: To investigate the role of expression patterns of well-defined asthma susceptibility genes during human and murine lung development. We hypothesized that genes influencing normal airways development would be over-represented by genes associated with asthma.Asthma genes were first identified via comprehensive search of the current literature. Next, we analyzed their expression patterns in the developing human lung during the pseudoglandular (gestational age, 7-16 weeks) and canalicular (17-26 weeks) stages of development, and in the complete developing lung time series of 3 mouse strains: A/J, SW, C57BL6.In total, 96 genes with association to asthma in at least two human populations were identified in the literature. Overall, there was no significant over-representation of the asthma genes among genes differentially expressed during lung development, although trends were seen in the human (Odds ratio, OR 1.22, confidence interval, CI 0.90-1.62) and C57BL6 mouse (OR 1.41, CI 0.92-2.11) data. However, differential expression of some asthma genes was consistent in both developing human and murine lung, e.g. NOD1, EDN1, CCL5, RORA and HLA-G. Among the asthma genes identified in genome wide association studies, ROBO1, RORA, HLA-DQB1, IL2RB and PDE10A were differentially expressed during human lung development.Our data provide insight about the role of asthma susceptibility genes during lung development and suggest common mechanisms underlying lung morphogenesis and pathogenesis of respiratory diseases.There is good evidence that genetic factors strongly influence the risk of asthma, and associations between numerous genes and asthma have been evaluated in the past decades [1,2]. Recent genome wide association studies (GWAS) of asthma have identified several additional asthma susceptibility genes [3-10]. Little is known about the role of most asthma susceptibility genes during human lung development.The "developmental origins" hypothesis [11] proposes that specific in uter
The CD4+ T-cell transcriptome and serum IgE in asthma: IL17RB and the role of sex
Gary M Hunninghake, Jen-hwa Chu, Sunita S Sharma, Michael H Cho, Blanca E Himes, Angela J Rogers, Amy Murphy, Vincent J Carey, Benjamin A Raby
BMC Pulmonary Medicine , 2011, DOI: 10.1186/1471-2466-11-17
Abstract: Peripheral blood CD4+ T cells from 223 participants from the Childhood Asthma Management Program (CAMP) with simultaneous measurement of IgE. Total RNA was isolated, and expression profiles were generated with Illumina HumanRef8 v2 BeadChip arrays. Modeling of the relationship between genome-wide gene transcript levels and IgE levels was performed in all subjects, and stratified by sex.Among all subjects, significant evidence for association between gene transcript abundance and IgE was identified for a single gene, the interleukin 17 receptor B (IL17RB), explaining 12% of the variance (r2) in IgE measurement (p value = 7 × 10-7, 9 × 10-3 after adjustment for multiple testing). Sex stratified analyses revealed that the correlation between IL17RB and IgE was restricted to males only (r2 = 0.19, p value = 8 × 10-8; test for sex-interaction p < 0.05). Significant correlation between gene transcript abundance and IgE level was not found in females. Additionally we demonstrated substantial sex-specific differences in IgE when considering multi-gene models, and in canonical pathway analyses of IgE level.Our results indicate that IL17RB may be the only gene expressed in CD4+ T cells whose transcript measurement is correlated with the variation in IgE level in asthmatics. These results provide further evidence sex may play a role in the genomic regulation of IgE.Total serum immunoglobulin E (IgE) is a risk factor for both the development of [1] and disease severity in asthma [2]. The production of IgE is controlled by a complex regulatory process that ultimately involves isotype class switching by mononuclear B lymphocytes, [3] a CD4+ T cell dependent process [3]. However, to our knowledge, there has been no analysis of the correlation between genome-wide CD4+ T cell gene expression and the variability in serum IgE among asthmatics.Sex is a critical determinant of IgE level, with males having a stronger tendency towards higher total IgE than females [4]. Sex-related differe
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