Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features.

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Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

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Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features.

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2015 nî lūn-bûn

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2015年の論文

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2015年論文

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2015年論文

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2015年論文

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Joint Analysis of Dependent Fe ...... tion of Differential Features.

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Joint Analysis of Dependent Fe ...... tion of Differential Features.

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Joint Analysis of Dependent Fe ...... tion of Differential Features.

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P1476

Joint Analysis of Dependent Fe ...... tion of Differential Features.

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Diana Trutschel

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Ivo Grosse

http://www.w3.org/2001/XMLSchema#string

Stephan Schmidt

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P2860

MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data

Statistical strategies for avoiding false discoveries in metabolomics and related experiments

Proposed minimum reporting standards for chemical analysis

Highly sensitive feature detection for high resolution LC/MS

XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification

The Generalization of `Student's' Problem when Several Different Population Variances are Involved

CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

The multifunctional enzyme CYP71B15 (PHYTOALEXIN DEFICIENT3) converts cysteine-indole-3-acetonitrile to camalexin in the indole-3-acetonitrile metabolic network of Arabidopsis thaliana.

Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data

A gentle guide to the analysis of metabolomic data.

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129

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scholarly article

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10.3389/FBIOE.2015.00129

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Steffen Neumann

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2015-09-24T00:00:00Z

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282286189

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26442246

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4585098

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Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features.

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P2860

P2860

Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features.

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