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Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses

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Title: Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses
Authors: Zou, X
Holmes, E
Nicholson, JK
Loo, RL
Item Type: Journal Article
Issue Date: 5-May-2016
Date of Acceptance: 5-May-2016
URI: http://hdl.handle.net/10044/1/34557
DOI: http://dx.doi.org/10.1021/acs.analchem.5b04020
ISSN: 1086-4377
Publisher: American Chemical Society
Start Page: 5670
End Page: 5679
Journal / Book Title: Analytical Chemistry
Volume: 88
Issue: 11
Copyright Statement: This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
Keywords: Science & Technology
Physical Sciences
Chemistry, Analytical
Chemistry
OPTIMIZED STATISTICAL APPROACH
BIOMARKER IDENTIFICATION
METABOLOMICS DATA
NMR-SPECTRA
NEPHROTOXICITY
HEPATOTOXICITY
VISUALIZATION
METABONOMICS
ASSOCIATION
VARIABILITY
Analytical Chemistry
0301 Analytical Chemistry
0904 Chemical Engineering
0399 Other Chemical Sciences
Publication Status: Published
Appears in Collections:Division of Surgery
Faculty of Medicine



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