Paper Info
Title
Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis.
Abstract
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.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.Neuroshell 2 is commercially available from Ward Systems.
Year
DOI
Venue
2005
10.1093/bioinformatics/bti368
Bioinformatics
Keywords
Field
DocType
separate validation subset,bacterial species,ward systems,proteomic data,cluster analysis,artificial neural network,clinical diagnosis,principal components analysis,bacterium neisseria meningitidis,tof mass spectrometry data,surface enhanced laser desorption ionization,principal component analysis
Proteomics,Computer science,Clinical diagnosis,Bioinformatics,Network analysis,Artificial neural network,Principal component analysis
Journal
Volume
Issue
ISSN
21
10
1367-4803
Citations 
PageRank 
References 
12
1.08
3
Authors
4
Name
Order
Citations
PageRank
Lee Lancashire1453.97
O Schmid2121.08
H Shah3121.08
G Ball4121.42