A community approach to mortality prediction in sepsis via gene expression analysis
暂无分享,去创建一个
Purvesh Khatri | Larsson Omberg | Marshall Nichols | Geoffrey S Ginsburg | Thanneer M Perumal | Benjamin Tang | Timothy E Sweeney | Lyle L Moldawer | Jesús F Bermejo-Martin | Raquel Almansa | Grant P Parnell | Ricardo Henao | S. Kingsmore | C. Woods | G. Ginsburg | P. Khatri | J. Knight | L. Mangravite | L. Omberg | L. Moldawer | T. Sweeney | H. Wong | E. Tsalik | A. Choi | T. Perumal | R. Langley | K. Burnham | R. J. Langley | Stephen F Kingsmore | C. Hinds | Ephraim L Tsalik | Raymond J Langley | Ricardo Henao | Christopher W Woods | Julian C Knight | Lara M Mangravite | R. Almansa | Emma E Davenport | Katie L Burnham | Charles J Hinds | B. Tang | Hector R Wong | E. Tamayo | Eduardo Tamayo | M. Nichols | Judith A Howrylak | Augustine M Choi | Frederick E Moore | J. Howrylak | G. Parnell | Ricardo Henao | B. Tang | E. Davenport | J. Bermejo-Martín | C. Hinds | Augustine M. Choi
[1] L. Mcphail,et al. Fueling the flame: bioenergy couples metabolism and inflammation , 2012, Journal of leukocyte biology.
[2] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[3] G. Escobar,et al. Hospital deaths in patients with sepsis from 2 independent cohorts. , 2014, JAMA.
[4] M. Hubank,et al. Oxidative phosphorylation gene expression falls at onset and throughout the development of meningococcal sepsis-induced multi-organ failure in children , 2015, Intensive Care Medicine.
[5] Anna Rautanen,et al. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study , 2016, The Lancet. Respiratory medicine.
[6] T. Sweeney,et al. Risk Stratification and Prognosis in Sepsis: What Have We Learned from Microarrays? , 2016, Clinics in chest medicine.
[7] M. Martín-Fernández,et al. Characterizing Systemic Immune Dysfunction Syndrome to Fill in the Gaps of SEPSIS-2 and SEPSIS-3 Definitions. , 2017, Chest.
[8] E. Xing,et al. Discovery of the gene signature for acute lung injury in patients with sepsis. , 2009, Physiological genomics.
[9] S. Kingsmore,et al. Gene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans , 2013, PloS one.
[10] J. Bermejo‐Martin,et al. Transcriptomic evidence of impaired immunoglobulin G production in fatal septic shock. , 2014, Journal of critical care.
[11] Lawrence Carin,et al. Host gene expression classifiers diagnose acute respiratory illness etiology , 2016, Science Translational Medicine.
[12] R. Gamelli,et al. Genomic responses in mouse models poorly mimic human inflammatory diseases , 2013, Proceedings of the National Academy of Sciences.
[13] J. Rello,et al. Pandemic Influenza , 2018, Emergency Medicine.
[14] Takaya Saito,et al. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.
[15] Purvesh Khatri,et al. A comprehensive time-course–based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set , 2015, Science Translational Medicine.
[16] J. Eiros,et al. Transcriptomic correlates of organ failure extent in sepsis. , 2015, The Journal of infection.
[17] Damien Chaussabel,et al. Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis , 2009, Genome Biology.
[18] Christine T. N. Pham,et al. Neutrophil serine proteases: specific regulators of inflammation , 2006, Nature Reviews Immunology.
[19] R. Bellomo,et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.
[20] Charity W. Law,et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.
[21] P. Schuetz,et al. The TRIAGE-ProADM Score for an Early Risk Stratification of Medical Patients in the Emergency Department - Development Based on a Multi-National, Prospective, Observational Study , 2016, PloS one.
[22] Peter Bühlmann. Regression shrinkage and selection via the Lasso: a retrospective (Robert Tibshirani): Comments on the presentation , 2011 .
[23] Matthew E. Ritchie,et al. limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.
[24] Juancarlos Chan,et al. Gene Ontology Consortium: going forward , 2014, Nucleic Acids Res..
[25] Mark Barnes,et al. A Global, Neutral Platform for Sharing Trial Data. , 2016, The New England journal of medicine.
[26] Matti Pirinen,et al. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis , 2016, Nature Communications.
[27] Thomas Yu,et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. , 2017, The Lancet. Oncology.
[28] S. Opal,et al. Sepsis: a roadmap for future research. , 2015, The Lancet. Infectious diseases.
[29] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.
[30] R. Tibshirani,et al. Regression shrinkage and selection via the lasso: a retrospective , 2011 .
[31] L. Schalkwyk,et al. Peripheral blood RNA gene expression profiling in patients with bacterial meningitis , 2013, Front. Neurosci..
[32] Haichao Wang,et al. PKM2 Regulates the Warburg Effect and Promotes HMGB1 Release in Sepsis , 2014, Nature Communications.
[33] Stephen Huang,et al. Aberrant Cell Cycle and Apoptotic Changes Characterise Severe Influenza A Infection – A Meta-Analysis of Genomic Signatures in Circulating Leukocytes , 2011, PloS one.
[34] J. Laake,et al. Excessive innate immune response and mutant D222G/N in severe A (H1N1) pandemic influenza. , 2011, The Journal of infection.
[35] C. Torio,et al. National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2011 , 2013 .
[36] Benjamin M. Bolstad,et al. affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..
[37] Li Liu,et al. A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis , 2016, PLoS Comput. Biol..
[38] B. Brumback,et al. Human Myeloid-derived Suppressor Cells are Associated With Chronic Immune Suppression After Severe Sepsis/Septic Shock , 2017, Annals of surgery.
[39] Michael G. Barnes,et al. Genome-level expression profiles in pediatric septic shock indicate a role for altered zinc homeostasis in poor outcome. , 2007, Physiological genomics.
[40] A. Ishizu,et al. NETosis markers: Quest for specific, objective, and quantitative markers. , 2016, Clinica chimica acta; international journal of clinical chemistry.
[41] D. Booth,et al. Transcriptional reprogramming of metabolic pathways in critically ill patients , 2016, Intensive Care Medicine Experimental.
[42] Francesco Vallania,et al. Methods to increase reproducibility in differential gene expression via meta-analysis , 2016, Nucleic acids research.
[43] Christopher W Seymour,et al. Developing a New Definition and Assessing New Clinical Criteria for Septic Shock: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.
[44] C. Lindsell,et al. Developing a clinically feasible personalized medicine approach to pediatric septic shock. , 2015, American journal of respiratory and critical care medicine.
[45] Henning Hermjakob,et al. The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..
[46] Minoru Kanehisa,et al. KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..
[47] Andreas Ziegler,et al. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.
[48] O. Gajic,et al. Diagnostic accuracy and clinical relevance of an inflammatory biomarker panel for sepsis in adult critically ill patients. , 2016, Diagnostic microbiology and infectious disease.
[49] Purvesh Khatri,et al. Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. , 2016, The Lancet. Respiratory medicine.
[50] D. Bolignano,et al. Prognostic models in the clinical arena , 2012, Aging Clinical and Experimental Research.
[51] David R. Booth,et al. Identifying Key Regulatory Genes in the Whole Blood of Septic Patients to Monitor Underlying Immune Dysfunctions , 2013, Shock.
[52] P. Schuetz,et al. Clinical scores and blood biomarkers for early risk assessment of patients presenting to the emergency department , 2014 .
[53] Michael Bailey,et al. Systemic inflammatory response syndrome criteria in defining severe sepsis. , 2015, The New England journal of medicine.
[54] S. Ryter,et al. Inflammasome-regulated cytokines are critical mediators of acute lung injury. , 2012, American journal of respiratory and critical care medicine.
[55] R. Kolamunnage-Dona,et al. Novel biomarker combination improves the diagnosis of serious bacterial infections in Malawian children , 2012, BMC Medical Genomics.
[56] N. Maugeri,et al. Instructive influences of phagocytic clearance of dying cells on neutrophil extracellular trap generation , 2015, Clinical and experimental immunology.
[57] B. Mougin,et al. Systemic transcriptional analysis in survivor and non-survivor septic shock patients: a preliminary study. , 2006, Immunology letters.
[58] M. Hubank,et al. Transcriptional Instability during Evolving Sepsis May Limit Biomarker Based Risk Stratification , 2013, PloS one.
[59] Justin Guinney,et al. GSVA: gene set variation analysis for microarray and RNA-Seq data , 2013, BMC Bioinformatics.
[60] R. James,et al. Validation of Virulence and Epidemiology DNA Microarray for Identification and Characterization of Staphylococcus aureus Isolates , 2008, Journal of Clinical Microbiology.
[61] Ewout W Steyerberg,et al. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers , 2011, Statistics in medicine.
[62] E. Seeley,et al. Increased expression of neutrophil-related genes in patients with early sepsis-induced ARDS. , 2015, American journal of physiology. Lung cellular and molecular physiology.
[63] Michael W. Weiner,et al. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease , 2016, Alzheimer's & Dementia.
[64] S. Friend,et al. Crowdsourcing biomedical research: leveraging communities as innovation engines , 2016, Nature Reviews Genetics.
[65] R. Xavier,et al. mTOR- and HIF-1α–mediated aerobic glycolysis as metabolic basis for trained immunity , 2014, Science.
[66] Lawrence Carin,et al. An integrated transcriptome and expressed variant analysis of sepsis survival and death , 2014, Genome Medicine.
[67] Edward Abraham,et al. New Definitions for Sepsis and Septic Shock: Continuing Evolution but With Much Still to Be Done. , 2016, JAMA.
[68] T. Miyakawa,et al. Genomic responses in mouse models poorly mimic human inflammatory diseases , 2013 .
[69] S. Opal,et al. The Next Generation of Sepsis Clinical Trial Designs: What Is Next After the Demise of Recombinant Human Activated Protein C?* , 2014, Critical care medicine.