Journal Article


J Havel
R O'Connor
A J Eustace
D M Collins
M Gottschalk
G Ivanova
D F Brougham



cell line cells neural networks computer principal component analysis humans metabolomics classification magnetic resonance spectroscopy reproducibility of results

Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. (2010)

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.
Collections Ireland -> Dublin City University -> PubMed

Full list of authors on original publication

J Havel, R O'Connor, A J Eustace, D M Collins, M Gottschalk, G Ivanova, D F Brougham

Experts in our system

Robert O'Connor
Dublin City University
Total Publications: 74
Alex J Eustace
Dublin City University
Denis M Collins
Dublin City University
Total Publications: 26