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Journal of Biomedicine and Biotechnology
Volume 2011 (2011), Article ID 158094, 8 pages
http://dx.doi.org/10.1155/2011/158094
Research Article

Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using 1H Nuclear Magnetic Resonance

1National Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland
2School of Chemical Sciences, Dublin City University, Dublin 9, Ireland
3REQUIMTE, Department of Chemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
4School of Nursing, Dublin City University, Dublin 9, Ireland
5Department of Chemistry, Masaryk University, 611 37 Brno, Czech Republic

Received 9 April 2010; Revised 14 June 2010; Accepted 23 July 2010

Academic Editor: Mika Ala-Korpela

Copyright © 2011 D. F. Brougham et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We report the successful classification, by artificial neural networks (ANNs), of 1H NMR spectroscopic data recorded on whole-cell culture samples of four different lung carcinoma cell lines, which display different drug resistance patterns. The robustness of the approach was demonstrated by its ability to classify the cell line correctly in 100% of cases, despite the demonstrated presence of operator-induced sources of variation, and irrespective of which spectra are used for training and for validation. The study demonstrates the potential of ANN for lung carcinoma classification in realistic situations.