research-article

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

Authors:

L. Lancashire,

O. Schmid,

H. Shah,

G. BallAuthors Info & Claims

Bioinformatics, Volume 21, Issue 10

Pages 2191 - 2199

https://doi.org/10.1093/bioinformatics/bti368

Published: 15 May 2005 Publication History

Abstract

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: [email protected]

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Wang JWang Y(2014)Mining for representative regions of virus genuses via protein sequences clusteringInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2014.0600549:3(321-337)Online publication date: 1-Mar-2014
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Oh JGao J(2011)Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass SpectraIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2010.428:6(1522-1534)Online publication date: 1-Nov-2011
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Ma JNguyen MRajapakse J(2009)Gene Classification Using Codon Usage and Support Vector MachinesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2007.702406:1(134-143)Online publication date: 1-Jan-2009
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Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis

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Bioinformatics Volume 21, Issue 10

May 2005

428 pages

ISSN:1367-4803

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Publisher

Oxford University Press, Inc.

United States

Publication History

Published: 15 May 2005

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Wang JWang Y(2014)Mining for representative regions of virus genuses via protein sequences clusteringInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2014.0600549:3(321-337)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1504/IJDMB.2014.060054
Oh JGao J(2011)Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass SpectraIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2010.428:6(1522-1534)Online publication date: 1-Nov-2011
https://dl.acm.org/doi/10.1109/TCBB.2010.42
Ma JNguyen MRajapakse J(2009)Gene Classification Using Codon Usage and Support Vector MachinesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2007.702406:1(134-143)Online publication date: 1-Jan-2009
https://dl.acm.org/doi/10.1109/TCBB.2007.70240
McDonald RSkipp PBennell JPotts CThomas LO'Connor C(2009)Mining whole-sample mass spectrometry proteomics data for biomarkers - An overviewExpert Systems with Applications: An International Journal10.1016/j.eswa.2008.06.13336:3(5333-5340)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1016/j.eswa.2008.06.133

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