Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using 1H Nuclear Magnetic Resonance
Joint Authors
Havel, J.
Gottschalk, M.
Ivanova, G.
Brougham, D. F.
Eustace, A. J.
Collins, D. M.
O'Connor, R.
Source
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-08-05
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
We report the successful classification, by artificial neural networks (ANNs), of H 1 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.
American Psychological Association (APA)
Brougham, D. F.& Ivanova, G.& Gottschalk, M.& Collins, D. M.& Eustace, A. J.& O'Connor, R.…[et al.]. 2010. Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using 1H Nuclear Magnetic Resonance. BioMed Research International،Vol. 2011, no. 2011, pp.1-8.
https://search.emarefa.net/detail/BIM-1027602
Modern Language Association (MLA)
Brougham, D. F.…[et al.]. Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using 1H Nuclear Magnetic Resonance. BioMed Research International No. 2011 (2011), pp.1-8.
https://search.emarefa.net/detail/BIM-1027602
American Medical Association (AMA)
Brougham, D. F.& Ivanova, G.& Gottschalk, M.& Collins, D. M.& Eustace, A. J.& O'Connor, R.…[et al.]. Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using 1H Nuclear Magnetic Resonance. BioMed Research International. 2010. Vol. 2011, no. 2011, pp.1-8.
https://search.emarefa.net/detail/BIM-1027602
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1027602