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Bioinformatics Advance Access originally published online on March 3, 2005
Bioinformatics 2005 21(10):2191-2199; doi:10.1093/bioinformatics/bti368
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis

L. Lancashire 1,2, O. Schmid 3, H. Shah 3 and G. Ball 1,2,*

1The Nottingham Trent University Nottingham, NG11 8NS, UK
2Loreus Ltd., School of Biomedical and Natural Sciences Clifton Campus, Clifton Lane, Nottingham, NG11 8NS, UK
3Molecular Identification Services Unit, Central Public Health Laboratory Health Protection Agency, 61 Colindale Avenue, London, NW9 5HT, UK

*To whom correspondence should be addressed.

Motivation: Robust computer algorithms are required to interpret the vast amounts of proteomic data currently being produced and to generate generalized models which are applicable to ‘real world’ scenarios. One such scenario is the classification of bacterial species. These vary immensely, some remaining remarkably stable whereas others are extremely labile showing rapid mutation and change. Such variation makes clinical diagnosis difficult and pathogens may be easily misidentified.

Results: We applied artificial neural networks (Neuroshell 2) in parallel with cluster analysis and principal components analysis to surface enhanced laser desorption/ionization (SELDI)-TOF mass spectrometry data with the aim of accurately identifying the bacterium Neisseria meningitidis from species within this genus and other closely related taxa. A subset of ions were identified that allowed for the consistent identification of species, classifying >97% of a separate validation subset of samples into their respective groups.

Availability: Neuroshell 2 is commercially available from Ward Systems.

Contact: graham.balls{at}ntu.ac.uk


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 J Med MicrobiolHome page
O. Schmid, G. Ball, L. Lancashire, R. Culak, and H. Shah
New approaches to identification of bacterial pathogens by surface enhanced laser desorption/ionization time of flight mass spectrometry in concert with artificial neural networks, with special reference to Neisseria gonorrhoeae
J. Med. Microbiol., December 1, 2005; 54(12): 1205 - 1211.
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