Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps

Authors:
Frank-Michael Schleif;Thomas Elssner;Markus Kostrzewa;Thomas Villmann;Barbara Hammer
Affiliations:
Bruker Daltonik GmbH, Germany;Bruker Daltonik GmbH, Germany;Bruker Daltonik GmbH, Germany;University Leipzig, Germany;Clausthal University of Technology, Germany
Venue:
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Year:
2006

Citing 0
Cited 1

Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps

WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We extend the self-organizing map in the variant as proposed by Heskes to a supervised fuzzy classification method. This leads to a robust classifier where efficient learning with fuzzy labeled or partially contradictory data is possible. Further, the integration of labeling into the location of prototypes in a self-organizing map leads to a visualization of those parts of the data relevant for the classification. The method is incorporated in a clinical proteomics toolkit dedicated for biomarker search which allows the necessary preprocessing and further data analysis with additional visualizations.