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

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5 Author(s)

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

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19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)

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