Abstract
In this paper we analyze two proteomic pattern datasets containing measurements from ovarian and prostate cancer samples. In particular, a linear and a quadratic support vector machine (SVM) are applied to the data for distinguishing between cancer and benign status. On the ovarian dataset SVM gives excellent results, while the prostate dataset seems to be a harder classification problem for SVM. The prostate dataset is futher analyzed by means of an evolutionary algorithm for feature selection (EAFS) that searches for small subsets of features in order to optimize the SVM performance. In general, the subsets of features generated by EAFS vary over different runs and over different data splitting in training and hold-out sets. Nevertheless, particular features occur more frequently over all the runs. The role of these “core” features as potential tumor biomarkers deserves further study.
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.
Similar content being viewed by others
References
Issaq, H.J., et al.: SELDI-TOF MS for diagnostic proteomics. Anal. Chem. 75(7), 148A–155A (2003)
Petricoin, E.F., et al.: Serum proteomic patterns for detection of prostate cancer. Journal of the National Cancer Institute 94(20), 1576–1578 (2002)
Petricoin, E.F., et al.: Use of proteomic patterns in serum to identify ovarian cancer. The Lancet 359(9306), 572–577 (2002)
Liu, H., Li, J., Wong, L.: A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Informatics 13, 51–60 (2002)
Ng, A.Y.: On feature selection: learning with exponentially many irrelevant features as training examples. In: Proc. 15th International Conf. on Machine Learning, pp. 404–412. Morgan Kaufmann, San Francisco (1998)
Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jong, K., Marchiori, E., van der Vaart, A. (2004). Analysis of Proteomic Pattern Data for Cancer Detection. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-24653-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21378-9
Online ISBN: 978-3-540-24653-4
eBook Packages: Springer Book Archive