title: Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance.
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.
authors.affiliation: National Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland. dermot.brougham@dcu.ie
authors.forename: D F
authors.initials: DF
authors.lastname: Brougham
authors.forename: G
authors.initials: G
authors.lastname: Ivanova
authors.forename: M
authors.initials: M
authors.lastname: Gottschalk
authors.forename: D M
authors.initials: DM
authors.lastname: Collins
authors.forename: A J
authors.initials: AJ
authors.lastname: Eustace
authors.forename: R
authors.initials: R
authors.lastname: O'Connor
authors.forename: J
authors.initials: J
authors.lastname: Havel
mesh_terms: D002460:Cell Line; D002477:Cells / Q000145:classification*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D015203:Reproducibility of Results