Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance.
AffiliationNational Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland. firstname.lastname@example.org
Local subject classificationMAGNETIC RESONANCE IMAGING
Magnetic Resonance Spectroscopy
Neural Networks (Computer)
Principal Component Analysis
Reproducibility of Results
MetadataShow full item record
CitationArtificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. 2011, 2011:158094 J. Biomed. Biotechnol.
JournalJournal of biomedicine & biotechnology
AbstractWe 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.
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