Clustering Dependencies with Support Vectors
Experimental technologies in molecular biology (particularly oligonucleotide and cDNA arrays) now make it possible to simultaneously measure mRna-levels for thousand of genes . One drawback is the difficulty in organizing this huge amount of data in functional structures (for instance, in cluster configurations); this can be useful to gain insight into regulatory processes or even for a statistical dimensionality reduction.
In this chapter, we consider a simplified model based on mRna-data only, which is an effective gene-to-gene interaction structure. This can provide at least a starting point for hypotheses generation for further data mining.
The chapter is organized as follows. In Sect. 11.2 we give a brief overview of the kernel methods and SVC algorithm. In Sect. 11.3 we address the MGRN problem and in Sect. 11.4 we apply our formulation to clustering the training set. In Sect. 11.5 we discuss the numerical results and finally, in Sect. 11.6, we conclude and discuss some directions for future work.
KeywordsSupport Vector Kernel Method Boolean Variable Cluster Boundary Support Vector Cluster
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- 6.Shawe-Taylor, J., Cristianini, N. (2004) Kernel Methods for Pattern Analysis. Cambridge University Press, UK.Google Scholar
- 7.Schölkopf, B., Smola, A.J., Muller, K.R. (1999) Advances in Kernel Method - Support Vector Learning. Cambridge, MA: MIT Press.Google Scholar
- 8.Schölkopf, B., Tsuda, K., Vert, J.P. (2004) Kernel Methods in Computational Biology. Cambridge, MA: MIT Press.Google Scholar
- 10.Gustafsson, M., Hörnquist, M., Lombardi, A. (2003) Large-scale reverse engineering by the lasso. Proceedings of International Conference on Systems Biology: 135–136.Google Scholar
- 11.Chen, T., Filkov, V., Skiena, S. (1999) Identifying gene regulatory networks from experimental data. Proceedings of the 3rd Annual International Conference on Computational Molecular Biology: 94–103.Google Scholar
- 12.Yang, J., Estivill-Castro, V., Chalup, S.K. (2002) Support vector custering trough proximity graph modelling. Proceedings of 9th International Conference on Neural Information Processing 2: 898–903.Google Scholar
- 13.Courant, R., Hilbert, D. (1953) Methods of Mathematical Physics, vol. 1. New York: Interscience.Google Scholar
- 15.Cook, S. (1971) The complexity of theorem prouvem procedures. Proceedings of the 3rd Symposium of the ACM on the Theory of Computing: 151–158.Google Scholar