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Villmann, T.* ; Schleif, F.M.* ; Kostrzewa, M.* ; Walch, A.K. ; Hammer, B.*

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

Brief. Bioinform. 9, 129-143 (2008)
Open Access Gold möglich sobald Verlagsversion bei der ZB eingereicht worden ist.
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 interpretation of the generated classifcation model. Further, the fuzzy-labeled self-organizing map is able to process uncertainty in data, and classification results can be obtained as fuzzy decisions. Moreover, this fuzzy classification together with the property of topographic mapping offers the possibility of class similarity detection, which can be used for class visualization. We demonstrate the power of both methods for two exemplary examples: the classification of bacteria (listeria types) and neoplastic and non-neoplastic cell populations in breast cancer tissue sections.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter classification; vector quantization; class visualization; machine learning; fuzzy-labeled self-organizing map; mass spectrometry
Sprache englisch
Veröffentlichungsjahr 2008
HGF-Berichtsjahr 2008
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Zeitschrift Briefings in Bioinformatics
Quellenangaben Band: 9, Heft: 2, Seiten: 129-143 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Pathology (PATH)
Research Unit Analytical Pathology (AAP)
POF Topic(s) 30504 - Mechanisms of Genetic and Environmental Influences on Health and Disease
30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-500300-001
G-500390-001
Erfassungsdatum 2008-05-27
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