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

Hdl Handle:
http://hdl.handle.net/10147/120848
Title:
Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance.
Authors:
Brougham, D F; Ivanova, G; Gottschalk, M; Collins, D M; Eustace, A J; O'Connor, R; Havel, J
Affiliation:
National Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland. dermot.brougham@dcu.ie
Citation:
Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. 2011, 2011:158094 J. Biomed. Biotechnol.
Journal:
Journal of biomedicine & biotechnology
Issue Date:
2011
URI:
http://hdl.handle.net/10147/120848
DOI:
10.1155/2011/158094
PubMed ID:
20886062
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.
Item Type:
Article
Language:
en
Keywords:
BIOTECHNOLOGY
Local subject classification:
MAGNETIC RESONANCE IMAGING; CELLS
MeSH:
Cell Line; Cells; Humans; Magnetic Resonance Spectroscopy; Metabolomics; Neural Networks (Computer); Principal Component Analysis; Reproducibility of Results
ISSN:
1110-7251

Full metadata record

DC FieldValue Language
dc.contributor.authorBrougham, D Fen
dc.contributor.authorIvanova, Gen
dc.contributor.authorGottschalk, Men
dc.contributor.authorCollins, D Men
dc.contributor.authorEustace, A Jen
dc.contributor.authorO'Connor, Ren
dc.contributor.authorHavel, Jen
dc.date.accessioned2011-02-01T16:06:03Z-
dc.date.available2011-02-01T16:06:03Z-
dc.date.issued2011-
dc.identifier.citationArtificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. 2011, 2011:158094 J. Biomed. Biotechnol.en
dc.identifier.issn1110-7251-
dc.identifier.pmid20886062-
dc.identifier.doi10.1155/2011/158094-
dc.identifier.urihttp://hdl.handle.net/10147/120848-
dc.description.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.-
dc.language.isoenen
dc.subjectBIOTECHNOLOGYen
dc.subject.meshCell Line-
dc.subject.meshCells-
dc.subject.meshHumans-
dc.subject.meshMagnetic Resonance Spectroscopy-
dc.subject.meshMetabolomics-
dc.subject.meshNeural Networks (Computer)-
dc.subject.meshPrincipal Component Analysis-
dc.subject.meshReproducibility of Results-
dc.subject.otherMAGNETIC RESONANCE IMAGINGen
dc.subject.otherCELLSen
dc.titleArtificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance.en
dc.typeArticleen
dc.contributor.departmentNational Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland. dermot.brougham@dcu.ieen
dc.identifier.journalJournal of biomedicine & biotechnologyen

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