Abstract
The paper presents results of the research verifying whether gene clustering that takes under consideration both gene expression values and similarity of GO terms improves a quality of rule-based description of the gene groups. The obtained results show that application of the Conditional Robust Fuzzy C-Medoids algorithm enables to obtain gene groups similar to the groups determined by domain experts. However, the differences observed in clustering influences a description quality of the groups. The rules determined cover more genes retaining their statistical significance. The rules induction and post-processing method presented in the paper takes under consideration, among others, a hierarchy of GO terms and a compound measure that evaluates the generated rules. The approach presented is unique, it makes possible to limit a number of rules determined considerably and to obtain rules that reflect varied biological knowledge even if they cover the same genes.
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
An, A., Cercone, N.: Rule quality measures for rule induction systems description and evaluation. Computational Intelligence 17, 409–424 (2001)
Ashburner, M., Ball, C.A., Blake, J.A., et al.: Gene ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000)
Azuaje, F., Wang, H., Bodenreider, O.: Ontology-driven similarity approaches to supporting gene functional assessment. In: Proceedings of the 18th Annual Bio-Ontologies Meeting, Michigan, US (2005)
Carmona-Sayez, P., Chagoyen, M., Rodriguez, A., Trelles, O., Carazo, J.M., Pascual-Montano, A.: Integerated analysis of gene expression by association rules discovery. BMC Bioinformatics 7(1), 54 (2006)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Science 95, 14, 863–14, 868 (1998)
Grzymala-Busse, J.W., Ziarko, W.: Data mining based on rough sets. In: Wang, J. (ed.) Data Mining Opportunities and Challenges, pp. 142–173. IGI Publishing, Hershey (2003)
Iyer, V.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., Lee, J.C., Trent, J.M., Staudt, L.M., Hudson, J., Boguski, M.S., Lashkari, D., Shalon, D., Botstein, D., Brown, P.O.: The transcriptional program in the response of human fibroblasts to serum. Science 283, 83–87 (1999)
Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.: Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems 9(4), 595–607 (2001)
Kustra, R., Zagdański, A.: Incorporating gene ontology in clustering gene expression data. In: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)
Loia, V., Pedrycz, W., Senatore, S.: P-FCM: a proximity-based fuzzy clustering for user-centered web applications. International Journal of Approximate Reasoning 34, 121–144 (2003)
Łęski, J., Czogała, E.: A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and its applications. Fuzzy Sets and Systems 108(3), 289–297 (1999)
Midelfart, H.: Supervised learning in gene ontology Part I: A rough set framework. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets IV. LNCS, vol. 3700, pp. 69–97. Springer, Heidelberg (2005)
Pedrycz, W.: Conditional fuzzy c-means. Pattern Recognition Letters 17, 625–631 (1996)
Sikora, M.: Rule quality measures in creation and reduction of data role models. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS, vol. 4259, pp. 716–725. Springer, Heidelberg (2006)
Stefanowski, J., Vanderpooten, D.: Induction of decision rules in classification and discovery-oriented perspectives. International Journal on Intelligent Systems 16(1), 13–27 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gruca, A., Kozielski, M., Sikora, M. (2009). Fuzzy Clustering and Gene Ontology Based Decision Rules for Identification and Description of Gene Groups. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-00563-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00562-6
Online ISBN: 978-3-642-00563-3
eBook Packages: EngineeringEngineering (R0)