Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods

@article{Villmann2008ClassificationOM,
  title={Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods},
  author={Thomas Villmann and Frank-Michael Schleif and Markus Kostrzewa and Axel Walch and Barbara Hammer},
  journal={Briefings in bioinformatics},
  year={2008},
  volume={9 2},
  pages={129-43}
}
In the present contribution we propose two recently developed classification algorithms for the analysis of mass-spectrometric data-the supervised neural gas and the fuzzy-labeled self-organizing map. The algorithms are inherently regularizing, which is recommended, for these spectral data because of its high dimensionality and the sparseness for specific problems. The algorithms are both prototype-based such that the principle of characteristic representants is realized. This leads to an easy… CONTINUE READING

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