Unified feature association networks through integration of transcriptomic and proteomic data
暂无分享,去创建一个
Ralph Baric | Joshua N Adkins | Katrina M Waters | Jason E McDermott | Ryan S McClure | Jason P Wendler | Jesica Swanstrom | Brooke L Deatherage Kaiser | Kristie L Oxford | R. Baric | J. Adkins | K. Waters | J. Mcdermott | R. Mcclure | Kristie L. Oxford | J. Wendler | B. D. Kaiser | Jesica A Swanstrom
[1] Jean YH Yang,et al. Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.
[2] Hans-Georg Kräusslich,et al. Comparative lipidomics analysis of HIV‐1 particles and their producer cell membrane in different cell lines , 2013, Cellular microbiology.
[3] Susumu Goto,et al. KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..
[4] Samuel H. Payne,et al. Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements* , 2014, Molecular & Cellular Proteomics.
[5] Kui Zhang,et al. Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways , 2017, PloS one.
[6] Shigehiko Kanaya,et al. Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach. , 2011, Omics : a journal of integrative biology.
[7] A. Nisalak,et al. Evidence that maternal dengue antibodies are important in the development of dengue hemorrhagic fever in infants. , 1988, The American journal of tropical medicine and hygiene.
[8] Guoping Zhao,et al. A Comprehensive Analysis of the Transcriptomes of Marssonina brunnea and Infected Poplar Leaves to Capture Vital Events in Host-Pathogen Interactions , 2015, PloS one.
[9] Ke Lu,et al. Missing data imputation by K nearest neighbours based on grey relational structure and mutual information , 2015, Applied Intelligence.
[10] Richard D. Smith,et al. Network Analysis of Epidermal Growth Factor Signaling Using Integrated Genomic, Proteomic and Phosphorylation Data , 2012, PloS one.
[11] Xi Chen,et al. Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network , 2017, Experimental and therapeutic medicine.
[12] Bor-Sen Chen,et al. Interspecies protein-protein interaction network construction for characterization of host-pathogen interactions: a Candida albicans-zebrafish interaction study , 2013, BMC Systems Biology.
[13] Julio R. Banga,et al. Enabling network inference methods to handle missing data and outliers , 2015, BMC Bioinformatics.
[14] V. Manivel,et al. Quantitative Proteomics and Lipidomics Analysis of Endoplasmic Reticulum of Macrophage Infected with Mycobacterium tuberculosis , 2015, International journal of proteomics.
[15] J. Mcdermott,et al. Separating the Drivers from the Driven: Integrative Network and Pathway Approaches Aid Identification of Disease Biomarkers from High-Throughput Data , 2010, Disease markers.
[16] Joshua N. Adkins,et al. Systems analysis of multiple regulator perturbations allows discovery of virulence factors in Salmonella , 2011, BMC Systems Biology.
[17] Qibin Zhang,et al. Temporal Proteome and Lipidome Profiles Reveal Hepatitis C Virus-Associated Reprogramming of Hepatocellular Metabolism and Bioenergetics , 2010, PLoS pathogens.
[18] Dan Gao,et al. Combining affinity propagation clustering and mutual information network to investigate key genes in fibroid , 2017, Experimental and therapeutic medicine.
[19] Joel G. Pounds,et al. Improved quality control processing of peptide-centric LC-MS proteomics data , 2011, Bioinform..
[20] James C. Schnable,et al. Integration of omic networks in a developmental atlas of maize , 2016, Science.
[21] K. Marchal,et al. Inferring the relation between transcriptional and posttranscriptional regulation from expression compendia , 2014, BMC Microbiology.
[22] Lei Fang,et al. Systematic analysis reveals a lncRNA-mRNA co-expression network associated with platinum resistance in high-grade serous ovarian cancer , 2018, Investigational New Drugs.
[23] Rafael A. Irizarry,et al. Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005 .
[24] Joel G. Pounds,et al. Combined Statistical Analyses of Peptide Intensities and Peptide Occurrences Improves Identification of Significant Peptides from MS-Based Proteomics Data , 2010, Journal of proteome research.
[25] Gianluca Bontempi,et al. minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information , 2008, BMC Bioinformatics.
[26] B. Tjaden,et al. The Gonococcal Transcriptome during Infection of the Lower Genital Tract in Women , 2015, PloS one.
[27] Joel G Pounds,et al. A statistical selection strategy for normalization procedures in LC‐MS proteomics experiments through dataset‐dependent ranking of normalization scaling factors , 2011, Proteomics.
[28] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[29] Christopher C. Overall,et al. Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002 , 2016, Nucleic acids research.
[30] J. Mateos,et al. Quantitative proteomic analysis of host—pathogen interactions: a study of Acinetobacter baumannii responses to host airways , 2015, BMC Genomics.
[31] S. Ferrari,et al. Hepatocyte growth factor favors monocyte differentiation into regulatory interleukin (IL)-10++IL-12low/neg accessory cells with dendritic-cell features. , 2006, Blood.
[32] Weidong Tian,et al. Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function , 2008, Genome Biology.
[33] Christopher J Petzold,et al. Lipidomics reveals control of Mycobacterium tuberculosis virulence lipids via metabolic coupling , 2007, Proceedings of the National Academy of Sciences.
[34] C. Nombela,et al. Proteomic profiling of serologic response to Candida albicans during host-commensal and host-pathogen interactions. , 2009, Methods in molecular biology.
[35] Xi Chen,et al. Global quantitative proteomic analysis profiles host protein expression in response to Sendai virus infection , 2017, Proteomics.
[36] G. Smyth,et al. Microarray background correction: maximum likelihood estimation for the normal–exponential convolution , 2008, Biostatistics.
[37] J. Adkins,et al. The landscape of viral proteomics and its potential to impact human health , 2016, Expert review of proteomics.
[38] J. Smit,et al. Dengue virus life cycle: viral and host factors modulating infectivity , 2010, Cellular and Molecular Life Sciences.
[39] B. Everitt,et al. An Introduction to Applied Multivariate Analysis with R , 2011 .
[40] Paul Pavlidis,et al. “Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks , 2012, PLoS Comput. Biol..
[41] Christopher C. Overall,et al. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality , 2015, Life.
[42] Tom Ross,et al. Integrated Transcriptomic and Proteomic Analysis of the Physiological Response of Escherichia coli O157:H7 Sakai to Steady-state Conditions of Cold and Water Activity Stress* , 2011, Molecular & Cellular Proteomics.
[43] Qihan Li,et al. Antibody-dependent enhancement of dengue virus infection inhibits RLR-mediated Type-I IFN-independent signalling through upregulation of cellular autophagy , 2016, Scientific Reports.
[44] Sophia Tsoka,et al. Gene Network and Proteomic Analyses of Cardiac Responses to Pathological and Physiological Stress , 2013, Circulation. Cardiovascular genetics.
[45] A. Maresso,et al. Global Metabolomic Analysis of a Mammalian Host Infected with Bacillus anthracis , 2015, Infection and Immunity.
[46] B. Finlay,et al. Impact of Salmonella Infection on Host Hormone Metabolism Revealed by Metabolomics , 2011, Infection and Immunity.
[47] Joachim Selbig,et al. Biological Cluster Evaluation for Gene Function Prediction , 2014, J. Comput. Biol..
[48] P. Geurts,et al. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.
[49] Chengjun Li,et al. The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus , 2016, BMC Systems Biology.
[50] C. Otth,et al. Transcriptomic analysis of responses to cytopathic bovine viral diarrhea virus-1 (BVDV-1) infection in MDBK cells. , 2016, Molecular immunology.
[51] Courtney Corley,et al. Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis , 2012, BMC Systems Biology.
[52] Ronald J. Moore,et al. Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer , 2016, Cell.
[53] C. Rogel-Gaillard,et al. Transcriptomic analysis of the dialogue between Pseudorabies virus and porcine epithelial cells during infection , 2008, BMC Genomics.
[54] Samuel H. Payne,et al. The utility of protein and mRNA correlation. , 2015, Trends in biochemical sciences.
[55] T. Salthouse. Do cognitive interventions alter the rate of age-related cognitive change? , 2015, Intelligence.
[56] H. Oshitani,et al. Novel insights into human respiratory syncytial virus-host factor interactions through integrated proteomics and transcriptomics analysis , 2016, Expert review of anti-infective therapy.
[57] S. Kalayanarooj,et al. Dengue virus (DENV) antibody-dependent enhancement of infection upregulates the production of anti-inflammatory cytokines, but suppresses anti-DENV free radical and pro-inflammatory cytokine production, in THP-1 cells. , 2007, The Journal of general virology.
[58] Mariano J. Alvarez,et al. An Integrated Systems Biology Approach Identifies TRIM25 as a Key Determinant of Breast Cancer Metastasis. , 2017, Cell reports.
[59] J. Collins,et al. Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.
[60] M. Fu,et al. A transcriptional miRNA-gene network associated with lung adenocarcinoma metastasis based on the TCGA database. , 2016, Oncology reports.
[61] Kieran J. Sharkey,et al. A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions , 2013, BMC Systems Biology.
[62] S. Brunke,et al. Dual-species transcriptional profiling during systemic candidiasis reveals organ-specific host-pathogen interactions , 2016, Scientific Reports.
[63] Matthew D. Dyer,et al. The Landscape of Human Proteins Interacting with Viruses and Other Pathogens , 2008, PLoS pathogens.
[64] Zhi-Liang Zheng,et al. Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to ‘Candidatus Liberibacter asiaticus’ infection , 2013, BMC Genomics.
[65] Mark Gerstein,et al. The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics , 2007, PLoS Comput. Biol..
[66] Young-Mo Kim,et al. A multi-omic systems approach to elucidating Yersinia virulence mechanisms. , 2013, Molecular bioSystems.
[67] Diogo M. Camacho,et al. Wisdom of crowds for robust gene network inference , 2012, Nature Methods.
[68] J. Mesirov,et al. The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .
[69] J. F. Pagotto,et al. Does the administration of pilocarpine prior to venom milking influence the composition of Micrurus corallinus venom? , 2018, Journal of proteomics.
[70] Hyunjin Yoon,et al. Bottlenecks and Hubs in Inferred Networks Are Important for Virulence in Salmonella typhimurium , 2009, J. Comput. Biol..
[71] Qibin Zhang,et al. A comprehensive collection of systems biology data characterizing the host response to viral infection , 2014, Scientific Data.