Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra
The analysis of functional data, is a common task in bioinformatics. Spectral data as obtained from mass spectrometric measurements in clinical proteomics are such functional data leading to new challenges for an appropriate analysis. Here we focus on the determination of classification models for such data. In general the available approaches for this task initially transform the spectra into a vector space followed by training a classifier. Hereby the functional nature of the data is typically lost, which may lead to suboptimal classifier models. Taking this into account a wavelet encoding is applied onto the spectral data leading to a compact functional representation. Further the Supervised Neural Gas classifier is extended by a functional metric. This allows the classifier to utilize the functional nature of the data in the modeling process. The presented method is applied to clinical proteom data showing good results.
Keywordssupervised neural gas functional data analysis clinical proteomics wavelet analysis spectra preprocessing
Unable to display preview. Download preview PDF.
- 4.Ketterlinus, R., Hsieh, S.-Y., Teng, S.-H., Lee, H., Pusch, W.: Fishing for biomarkers: analyzing mass spectrometry data with the new clinprotools software. Bio. techniques 38(6), 37–40 (2005)Google Scholar
- 6.Lee, J., Verleysen, M.: Generalizations of the lp norm for time series and its application to self-organizing maps. In: Cottrell, M. (ed.) 5th Workshop on Self-Organizing Maps, vol. 1, pp. 733–740 (2005)Google Scholar
- 9.Sato, A., Yamada, K.: Generalized learning vector quantization. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Proceedings of the 1995 Conference, Advances in Neural Information Processing Systems 8, pp. 423–429. MIT Press, Cambridge (1996)Google Scholar
- 11.Villmann, T., Hammer, B.: Supervised neural gas for learning vector quantization. In: Polani, D., Kim, J., Martinetz, T. (eds.) Proc. of the 5th German Workshop on Artificial Life (GWAL-5), pp. 9–16. IOS Press, Berlin (2002)Google Scholar
- 12.Waagen, D.E., Cassabaum, M.L., Scott, C., Schmitt, H.A.: Exploring alternative wavelet base selection techniques with application to high resolution radar classification. In: Proc. of the 6th Int. Conf. on Inf. Fusion (ISIF’03), pp. 1078–1085. IEEE Press, New York (2003)Google Scholar