title: Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  Neuroshell 2 is commercially available from Ward Systems.
authors.affiliation: The Nottingham Trent University, Nottingham, NG11 8NS, UK.
authors.forename: L
authors.initials: L
authors.lastname: Lancashire
authors.forename: O
authors.initials: O
authors.lastname: Schmid
authors.forename: H
authors.initials: H
authors.lastname: Shah
authors.forename: G
authors.initials: G
authors.lastname: Ball
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000032:analysis* / Q000378:metabolism; D015415:Biomarkers / Q000032:analysis / Q000378:metabolism; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods; D008954:Models, Biological; D009345:Neisseria meningitidis / Q000145:classification* / Q000302:isolation & purification / Q000378:metabolism*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis; D020543:Proteome / Q000032:analysis* / Q000378:metabolism; D013045:Species Specificity
chemical_list: D001426:Bacterial Proteins; D015415:Biomarkers; D020543:Proteome