An automated Pearson's correlation change classification (APC3) approach for GC/MS metabonomic data using total ion chromatograms (TICs)

A fully automated and computationally efficient Pearson's correlation change classification (APC3) approach is proposed and shown to have overall comparable performance with both an average accuracy and an average AUC of 0.89 ± 0.08 but is 3.9 to 7 times faster, easier to use and have low outli...

Full description

Saved in:
Bibliographic Details
Published inAnalyst (London) Vol. 138; no. 10; pp. 2883 - 2889
Main Authors Prakash, Bhaskaran David, Esuvaranathan, Kesavan, Ho, Paul C, Pasikanti, Kishore Kumar, Chan, Eric Chun Yong, Yap, Chun Wei
Format Journal Article
LanguageEnglish
Published England 21.05.2013
Subjects
Algorithms
Automation
Classification
Computational efficiency
Computer programs
Computer Simulation
Correlation
Gas Chromatography-Mass Spectrometry
Outliers (statistics)
Software
Spectra
Online AccessGet full text

Cover

More Information
Summary:A fully automated and computationally efficient Pearson's correlation change classification (APC3) approach is proposed and shown to have overall comparable performance with both an average accuracy and an average AUC of 0.89 ± 0.08 but is 3.9 to 7 times faster, easier to use and have low outlier susceptibility in contrast to other dimensional reduction and classification combinations using only the total ion chromatogram (TIC) intensities of GC/MS data. The use of only the TIC permits the possible application of APC3 to other metabonomic data such as LC/MS TICs or NMR spectra. A RapidMiner implementation is available for download at http://padel.nus.edu.sg/software/padelapc3.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0003-2654
1364-5528
DOI:10.1039/c3an00048f