Abstract
Gene expression data are numerical and describe the level of expression of genes in different situations, thus featuring behaviour of the genes. Two methods based on FCA (Formal Concept Analysis) are considered for clustering gene expression data. The first one is based on interordinal scaling and can be realized using standard FCA algorithms. The second method is based on pattern structures and needs adaptations of standard algorithms to computing with interval algebra. The two methods are described in details and discussed. The second method is shown to be more computationally efficient and providing more readable results. Experiments with gene expression data are discussed.
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
Stoughton, R.B.: Applications of DNA microarrays in biology. Annual Review of Biochemistry 74(1), 53–82 (2005)
Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: a survey. IEEE Trans. on Knowledge and Data Engineering 16(11), 1370–1386 (2004)
Madeira, S., Oliveira, A.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1(1), 24–45 (2004)
Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)
Blachon, S., Pensa, R., Besson, J., Robardet, C., Boulicaut, J.F., Gandrillon, O.: Clustering formal concepts to discover biologically relevant knowledge from gene expression data. In Silico Biology 7(4–5), 467–483 (2007)
Kaytoue, M., Duplessis, S., Napoli, A.: Using formal concept analysis for the extraction of groups of co-expressed genes. In: An, L.T.H., Bouvry, P., Tao, P.D. (eds.) Modelling, Computation and Optimization in Information Systems and Management Sciences (2008)
Motameny, S., Versmold, B., Schmutzler, R.: Formal concept analysis for the identification of combinatorial biomarkers in breast cancer. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 229–240. Springer, Heidelberg (2008)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. 8th International Conference on Intelligent Systems for Molecular Biology (ISBM), pp. 93–103 (2000)
Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(suppl. 1), S136–S144 (2002)
Robardet, C., Pensa, R.G., Besson, J., Boulicaut, J.-F.: Using classification and visualization on pattern databases for gene expression data analysis. In: Proceedings of the Intl. Workshop on Pattern Representation and Management, Heraklion, Hellas, March 18 (2004)
Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Berlin (1999)
Choi, V., Huang, Y., Lam, V., Potter, D., Laubenbacher, R., Duca, K.: Using formal concept analysis for microarray data comparison. J. Bioinform. Comput. Biol. 6(1), 65–75 (2008)
Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS 2001. LNCS, vol. 2120, pp. 129–142. Springer, Heidelberg (2001)
Kuznetsov, S.O.: JSM-method as a machine learning method. Itogi Nauki i Tekhniki, ser. Informatika 15, 17–50 (1991)
Kuznetsov, S.O., Obiedkov, S.A.: Comparing performance of algorithms for generating concept lattices. J. Exp. Theor. Artif. Intell. 14(2-3), 189–216 (2002)
Pensa, R.G., Besson, J., Boulicaut, J.F.: A methodology for biologically relevant pattern discovery from gene expression data. Discovery Science, 230–241 (2004)
Messai, N., Devignes, M.D., Napoli, A., Smaïl-Tabbone, M.: Many-valued concept lattices for conceptual clustering and information retrieval. In: 18th biennial European Conference on Artificial Intelligence (2008)
Chaudron, L., Maille, N.: Generalized formal concept analysis. In: International Conference on Conceptual Structures (ICCS), pp. 357–370 (2000)
Ferré, S., Ridoux, O.: A logical generalization of formal concept analysis. In: International Conference on Conceptual Structures (ICCS), pp. 371–384 (2000)
Martin, F.: The genome of laccaria bicolor provides insights into mycorrhizal symbiosis. Nature 452(7183), 88–92 (2008); 69 co-authors wrote this paper
Busch, S., Braus, G.H.: How to build a fungal fruit body: from uniform cells to specialized tissue. Molecular Microbiology 64, 873–876 (2007)
Lacourt, I., Duplessis, S., Abba, S., Bonfante, P., Martin, F.: Isolation and characterization of differentially expressed genes in the mycelium and fruit body of tuber borchii. Applied and Environmental Microbiology 68, 4574–4582 (2002)
Gabella, S., Abba, S., Duplessis, S., Montanini, B., Martin, F., Bonfante, P.: Transcript profiling reveals novel marker genes involved in fruiting body formation in tuber borchii. Eukaryotic Cell 4, 1599–1602 (2005)
Hsiao, C.J., Zaki, M.J.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans. on Knowl. and Data Eng. 17(4), 462–478 (2005)
Szathmary, L.: Symbolic Data Mining Methods with the Coron Platform. PhD Thesis in Computer Science, Univ. Henri Poincaré – Nancy 1, France (November 2006)
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
Kaytoue, M., Duplessis, S., Kuznetsov, S.O., Napoli, A. (2009). Two FCA-Based Methods for Mining Gene Expression Data. In: Ferré, S., Rudolph, S. (eds) Formal Concept Analysis. ICFCA 2009. Lecture Notes in Computer Science(), vol 5548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01815-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-01815-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01814-5
Online ISBN: 978-3-642-01815-2
eBook Packages: Computer ScienceComputer Science (R0)