Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using 1H Nuclear Magnetic Resonance. (15th September 2010)

• Record Type: Journal Article
• Authors: Brougham, D. F.; Ivanova, G.; Gottschalk, M.; Collins, D. M.; Eustace, A. J.; O'Connor, R.; Havel, J.
• Other Names: Ala-Korpela Mika Academic Editor.
• Is Part Of: Journal of biomedicine and biotechnology. Volume 2011(2011)
• Publication Date: 2010-09-15
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Medicine; Biology; Biotechnology; Médecine; Biologie; Biotechnologie; Biology; Biotechnology; Medicine; Biotechnology; Biomedicine; Electronic journals; Periodical; Periodicals; Electronic journals; 610
• DOI: 10.1155/2011/158094
• Languages: English
• ISSNs: 1110-7243

Abstract

Abstract : We report the successful classification, by artificial neural networks (ANNs), of 1 H 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.