A statistical framework for biomarker discovery in metabolomic time course data.
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Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical ActivityTime Dependency of Chemodiversity and Biosynthetic Pathways: An LC-MS Metabolomic Study of Marine-Sourced PenicilliumA weighted relative difference accumulation algorithm for dynamic metabolomics data: long-term elevated bile acids are risk factors for hepatocellular carcinoma.A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' DataA correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation.A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.Real-time digitization of metabolomics patterns from a living system using mass spectrometry.MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approachMetabolomics for clinical use and research in chronic kidney disease.Childhood tuberculosis is associated with decreased abundance of T cell gene transcripts and impaired T cell function.Mycobacterium tuberculosis Exploits a Molecular Off Switch of the Immune System for Intracellular Survival.How to model temporal changes in nontargeted metabolomics study? A Bayesian multilevel perspective.Analysis of Raw Biofluids by Mass Spectrometry Using Microfluidic Diffusion-Based Separation.
P2860
Q28387059-CD4C38A1-063A-48FD-A07B-69B3C083D943Q28833262-0802F1A1-1866-48F9-A54A-37B7E61A2AB3Q30908240-28BE697F-1A8C-4C34-B676-E76313D14F9DQ30990318-E70683BB-F10A-4C5B-9D5F-1D5C1982EF10Q30991262-9CF77451-5B95-4CAE-99B9-23BE146754ACQ31125724-C89EE39F-3979-49DD-81EF-4ABF206C2FD6Q34175477-03400A78-C73C-456F-A84E-B6B502091A74Q35049837-3004A6C6-9619-4D04-8082-916CA29ED566Q38750287-D6F4AFAA-AADE-4554-AB8C-29B7B31A8BAEQ46260766-DBACC9DA-A55D-4FB6-A241-D0C40918BB16Q47657805-1647311D-254B-4169-909E-40C01DB39259Q47730270-36C5C995-177A-42D3-A68D-3A8402D54EDEQ47917964-F3C005C4-D11B-4ACA-9E17-9C2D46213817
P2860
A statistical framework for biomarker discovery in metabolomic time course data.
description
2011 nî lūn-bûn
@nan
2011 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
A statistical framework for biomarker discovery in metabolomic time course data.
@ast
A statistical framework for biomarker discovery in metabolomic time course data.
@en
type
label
A statistical framework for biomarker discovery in metabolomic time course data.
@ast
A statistical framework for biomarker discovery in metabolomic time course data.
@en
prefLabel
A statistical framework for biomarker discovery in metabolomic time course data.
@ast
A statistical framework for biomarker discovery in metabolomic time course data.
@en
P2093
P2860
P356
P1433
P1476
A statistical framework for biomarker discovery in metabolomic time course data.
@en
P2093
Giovanni Montana
Maurice Berk
Timothy Ebbels
P2860
P304
P356
10.1093/BIOINFORMATICS/BTR289
P407
P577
2011-07-01T00:00:00Z