Mining whole-sample mass spectrometry proteomics data for biomarkers - An overview
Expert Systems with Applications: An International Journal
Gene Classification Using Codon Usage and Support Vector Machines
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Artificial Neural Network Based Algorithm for Biomolecular Interactions Modeling
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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@ntu.ac.uk