Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rieder, A., Louis, A.K., Maaß, P.: Wavelets: Theory and Applications. Wiley, Chichester (1998)
Cohen, A., Daubechies, I., Feauveau, J.-C.: Biorthogonal bases of compactly supported wavelets. Comm. Pure Appl. Math. 45(5), 485–560 (1992)
Hammer, B., Strickert, M., Villmann, T.: Supervised neural gas with general similarity measure. Neural Processing Letters 21(1), 21–44 (2005)
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)
Kohonen, T.: Self-Organizing Maps, 2nd ext. edn. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (1997)
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)
Leung, A., Chau, F., Gao, J.: A review on applications of wavelet transform techniques in chemical analysis: 1989-1997. Chem. and Int. Lab. Sys. 43(1), 165–184 (1998)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: ’Neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Trans. on Neural Networks 4(4), 558–569 (1993)
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)
Villanueva, J., Philip, J., Entenberg, D., Chaparro, C.A., et al.: Serum peptide profiling by magnetic particle-assisted, automated sample processing and malditof mass spectrometry. Anal. Chem. 76, 1560–1570 (2004)
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)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schleif, FM., Villmann, T., Hammer, B. (2007). Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_125
Download citation
DOI: https://doi.org/10.1007/978-3-540-73007-1_125
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
eBook Packages: Computer ScienceComputer Science (R0)