Experimental technologies in molecular biology (particularly oligonucleotide and cDNA arrays) now make it possible to simultaneously measure mRna-levels for thousand of genes [1]. 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.
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
Eisen, M., Brown, P. (1999) Dna arrays for analysis of gene expression. Methods in Enzymology 303: 179–205.
Bittner, M., Meltzer, P., Trent, J. (1999) Data analysis and integration: Of steps and arrows. Nature Genetics 22: 213–215.
Chen, Y., Bittner, M.L., Rougherty, E.R. (1999) Issues associated with microarray data analysis and integration. Nature Genetics 22: 213–215.
Heyer, L.J., Kruglyak, S., Yooseph, S. (1999) Exploring expression data: Identification and analysis of coexpressed genes. Genome Research 9: 1106–1115.
Filkov, V., Skiena, S., Zhi, J. (2002) Analysis techniques for microarray time-series data. Journal of Computational Biology 9: 317–330.
Shawe-Taylor, J., Cristianini, N. (2004) Kernel Methods for Pattern Analysis. Cambridge University Press, UK.
Schölkopf, B., Smola, A.J., Muller, K.R. (1999) Advances in Kernel Method - Support Vector Learning. Cambridge, MA: MIT Press.
Schölkopf, B., Tsuda, K., Vert, J.P. (2004) Kernel Methods in Computational Biology. Cambridge, MA: MIT Press.
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V. (2001) Support vector clustering. Journal of Machine Learning Research 2: 125–137.
Gustafsson, M., Hörnquist, M., Lombardi, A. (2003) Large-scale reverse engineering by the lasso. Proceedings of International Conference on Systems Biology: 135–136.
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.
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.
Courant, R., Hilbert, D. (1953) Methods of Mathematical Physics, vol. 1. New York: Interscience.
Pozzi, S., Della Vedova, G., Mauri, G. (2005) An explicit upper bound for the approximation ratio of the maximum gene regulatory network problem. Proceedings on CMSB, 3082: 1–8.
Cook, S. (1971) The complexity of theorem prouvem procedures. Proceedings of the 3rd Symposium of the ACM on the Theory of Computing: 151–158.
Cho, R., Campbell, M., Winzeler, E., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Gabrielian, A., Landsman, D., Lockhart, D., Davis, R. (1998) A genomic-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2: 65–73.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Zoppis, I., Mauri, G. (2008). Clustering Dependencies with Support Vectors. In: Castillo, O., Xu, L., Ao, SI. (eds) Trends in Intelligent Systems and Computer Engineering. Lecture Notes in Electrical Engineering, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74935-8_11
Download citation
DOI: https://doi.org/10.1007/978-0-387-74935-8_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74934-1
Online ISBN: 978-0-387-74935-8
eBook Packages: EngineeringEngineering (R0)