Please use this identifier to cite or link to this item: https://doi.org/10.1039/c3an00048f
Title: An automated Pearson's correlation change classification (APC3) approach for GC/MS metabonomic data using total ion chromatograms (TICs)
Authors: Prakash, B.D.
Esuvaranathan, K.
Ho, P.C. 
Pasikanti, K.K.
Yong Chan, E.C. 
Yap, C.W. 
Issue Date: 21-May-2013
Source: Prakash, B.D., Esuvaranathan, K., Ho, P.C., Pasikanti, K.K., Yong Chan, E.C., Yap, C.W. (2013-05-21). An automated Pearson's correlation change classification (APC3) approach for GC/MS metabonomic data using total ion chromatograms (TICs). Analyst 138 (10) : 2883-2889. ScholarBank@NUS Repository. https://doi.org/10.1039/c3an00048f
Abstract: 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. © 2012 The Royal Society of Chemistry.
Source Title: Analyst
URI: http://scholarbank.nus.edu.sg/handle/10635/105634
ISSN: 00032654
DOI: 10.1039/c3an00048f
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