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Bioinformatics Advance Access published online on March 3, 2005
Bioinformatics, 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@oupjournals.org
Received October 29, 2004
Revised February 23, 2005
Accepted February 28, 2005

Article

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

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

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

* To whom correspondence should be addressed.
G. Ball, E-mail: Graham.Balls{at}ntu.ac.uk


  Abstract

Motivation: Robust computer algorithms are required to interpret the vast amounts of proteomic data currently being produced, and to generate generalised 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 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 greater than 97% of a separate validation subset of samples into their respective groups.

Availability: Neuroshell 2 is commercially available from Ward Systems.


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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.
[Abstract] [Full Text] [PDF]


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