Standard machine learning algorithms applied to UPLC-TOF/MS metabolic fingerprinting for the discovery of wound biomarkers in Arabidopsis thaliana

Chemometrics and Intelligent Laboratory Systems

Volumn 104, Issue 1, 2010, Pages 20-27

  Boccard, Julien ^a   Kalousis, Alexandros ^a   Hilario, Melanie ^a   Lantéri, Pierre ^b   Hanafi, Mohamed ^c   Mazerolles, Gérard ^d   Wolfender, Jean Luc ^a   Carrupt, Pierre Alain ^a   Rudaz, Serge ^a  

^a UNIVERSITY OF GENEVA   (Switzerland)

^b UNIVERSITÉ LYON 1   (France)

^c LUNAM UNIVERSITÉ   (France)

^d SPO   (France)

Author keywords

Arabidopsis thaliana; Data mining; Machine learning; Mass spectrometry; Metabolomics; UPLC MS

Indexed keywords

BIOLOGICAL MARKER;

ARABIDOPSIS; ARTICLE; CLASSIFICATION ALGORITHM; DATA ANALYSIS; MACHINE LEARNING; METABOLOMICS; NONHUMAN; PHYTOCHEMISTRY; PLANT DISEASE; PLANT METABOLISM; PLANT STRESS; PRIORITY JOURNAL; RELIABILITY; STANDARDIZATION; STATISTICAL MODEL; TIME OF FLIGHT MASS SPECTROMETRY; ULTRA PERFORMANCE LIQUID CHROMATOGRAPHY;

EID: 78650173485     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2010.03.003     Document Type: Article

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