about
Metabolic surgery: action via hormonal milieu changes, changes in bile acids or gut microbiota? A summary of the literatureA comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene setPrediction of neurosurgical intervention after mild traumatic brain injury using the national trauma data bank.Learning statistical models of phenotypes using noisy labeled training data.Benchmarking Sepsis Gene Expression Diagnostics Using Public Data.Differential regulation of the PGC family of genes in a mouse model of Staphylococcus aureus sepsisA toll-like receptor 2 pathway regulates the Ppargc1a/b metabolic co-activators in mice with Staphylococcal aureus sepsisThe human gut microbiome: a review of the effect of obesity and surgically induced weight loss.Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis.Combined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL): A Robust Method for Selection of Cluster Number, K.Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory VirusesRisk Stratification and Prognosis in Sepsis: What Have We Learned from Microarrays?Complement pathway amplifies caspase-11-dependent cell death and endotoxin-induced sepsis severity.EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY.Methods to increase reproducibility in differential gene expression via meta-analysis.Robust classification of bacterial and viral infections via integrated host gene expression diagnostics.Using the wisdom of the crowds to find critical errors in biomedical ontologies: a study of SNOMED CT.Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury.Unique transcriptomic response to sepsis is observed among patients of different age groups.Circulating Biomarkers to Identify Responders in Cardiac Cell therapy.Generalizable Biomarkers in Critical Care: Toward Precision Medicine.Septic Cardiomyopathy: Getting to the Heart of the Matter.Hospital-acquired Pneumonia: A Host of Factors.Co-regulation of nuclear respiratory factor-1 by NFkappaB and CREB links LPS-induced inflammation to mitochondrial biogenesis.Comprehensive Validation of the FAIM3:PLAC8 Ratio in Time-matched Public Gene Expression Data.Blood transcriptional signatures for tuberculosis diagnosis: a glass half-empty perspective - Authors' reply.Simplification of a Septic Shock Endotyping Strategy for Clinical Application.Encouraging Mindfulness in Medical House Staff via Smartphone App: A Pilot Study.Multicohort Analysis of Whole-Blood Gene Expression Data Does Not Form a Robust Diagnostic for Acute Respiratory Distress Syndrome.Pediatric Sepsis Endotypes Among Adults With Sepsis.A community approach to mortality prediction in sepsis via gene expression analysis.Author Correction: Circulating Biomarkers to Identify Responders in Cardiac Cell therapy.A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infectionsDiagnosis of bacterial sepsis: why are tests for bacteremia not sufficient?The authors replyThe authors reply
P50
Q27010589-3F134920-C9CB-43BE-B630-741D121AE979Q27325510-0D355F83-425F-4D7F-9624-58F1846013DFQ30968916-99E74E5E-7C00-4BBB-9F09-77AF5F4F8AEAQ31095809-D53BED5E-4C19-417D-B17E-F933DEDA9771Q31133607-B9DDB840-E32C-428F-911D-65A904F59BE1Q33640312-3767AA68-38F7-44A5-AB0D-1E4C110BF7EBQ33640312-D70D2D85-A3A1-404A-8163-469AA0D7A716Q34038107-420B171E-D218-47DA-A8CE-70893C2E8240Q35360260-F6495128-FC78-43A2-9F71-2C6573D6C12EQ35932980-B8ABB064-51E3-4BAD-9413-EAE18DD9B2F7Q36297983-ACDBE155-59C1-4CFA-AF9F-8B3901B09CE5Q36386754-E98D2C2B-C5DE-4A8B-ABA0-2211F385A286Q36945095-97992D1F-5CCF-442D-8B79-47222C881B6FQ37346659-3CDD4918-9568-498B-8BCF-34BE921B31B0Q37515773-001BEF53-45E7-48A8-B19E-FB46C5344791Q37577407-ADB7DD00-370F-4B5E-859C-06FA87603136Q37698010-7B1D4CC7-0A8A-42BD-8B4B-C9282EA58C36Q38262429-26EDF4E5-3B44-4141-825A-A254383F6573Q38668056-279235A9-B18E-4CC2-B7DB-01B3783293DBQ38889518-6E1E7DDD-0300-4231-A686-6F2E0E300F82Q40060306-2318C304-35D4-4CFD-B138-427CF6EA863DQ40138868-D42EE2D2-CE8E-4E26-9313-768F1D4DDBDAQ40210673-EDF494AC-F064-4DF8-8FAB-5B87023F95D1Q40328194-73CD7B23-2EFF-48A4-96E0-0F8CA91B8A2AQ40427923-09A7F50A-8BC3-44CD-8EE5-FEF425DEC98FQ41767904-4B7035A2-8ED3-495D-9938-D143BFBAFD66Q43062894-72380DFF-BCB3-4ACA-BF95-6458B1FF76D8Q44315224-72785340-9146-4D6F-AED8-AD743A8C50F1Q44547938-0E56E7F7-46CC-4442-9D48-9B415E633C67Q47264855-11E443F1-6C64-478D-88F3-8C16E7D91CD3Q47644676-ECC296F2-18D3-412E-B055-A25703A46149Q47836768-F0F37FCF-D80B-4809-9C40-5BD9BEB02543Q49959166-49E5E6D2-FE88-47CD-8A7C-3DB0547292F6Q55345737-DFD29C31-CF8C-4B10-9DDB-D09AEFAADFCEQ90052806-29B2C57E-E40F-4410-9230-8F89065EC98FQ92851912-3FD2CEAB-ECAA-4C96-BEA4-179E84DB7581Q94424757-A8083263-AC33-4A81-92CE-4F49C7846EB0Q95480574-BDC2900C-CBBE-4F4B-A916-8A11326AF122
P50
description
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Timothy E Sweeney
@ast
Timothy E Sweeney
@en
Timothy E Sweeney
@es
Timothy E Sweeney
@nl
type
label
Timothy E Sweeney
@ast
Timothy E Sweeney
@en
Timothy E Sweeney
@es
Timothy E Sweeney
@nl
prefLabel
Timothy E Sweeney
@ast
Timothy E Sweeney
@en
Timothy E Sweeney
@es
Timothy E Sweeney
@nl
P106
P1153
35752498100
P31
P496
0000-0002-3596-1093