Document Detail


Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance.
MedLine Citation:
PMID:  20886062     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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:
D F Brougham; G Ivanova; M Gottschalk; D M Collins; A J Eustace; R O'Connor; J Havel
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2010-09-15
Journal Detail:
Title:  Journal of biomedicine & biotechnology     Volume:  2011     ISSN:  1110-7251     ISO Abbreviation:  J. Biomed. Biotechnol.     Publication Date:  2011  
Date Detail:
Created Date:  2010-10-04     Completed Date:  2011-01-13     Revised Date:  2013-07-03    
Medline Journal Info:
Nlm Unique ID:  101135740     Medline TA:  J Biomed Biotechnol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  158094     Citation Subset:  IM    
Affiliation:
National Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland. dermot.brougham@dcu.ie
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MeSH Terms
Descriptor/Qualifier:
Cell Line
Cells / classification*
Humans
Magnetic Resonance Spectroscopy / methods*
Metabolomics / methods*
Neural Networks (Computer)*
Principal Component Analysis
Reproducibility of Results
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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