Abstract
One of the most exciting scientific challenges in functional genomics concerns the discovery of biologically relevant patterns from gene expression data. For instance, it is extremely useful to provide putative synexpression groups or transcription modules to molecular biologists. We propose a methodology that has been proved useful in real cases. It is described as a prototypical KDD scenario which starts from raw expression data selection until useful patterns are delivered. Our conceptual contribution is (a) to emphasize how to take the most from recent progress in constraint-based mining of set patterns, and (b) to propose a generic approach for gene expression data enrichment. The methodology has been validated on real data sets.
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
DeRisi, J., Iyer, V., Brown, P.: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997)
Niehrs, C., Pollet, N.: Synexpression groups in eukaryotes. Nature 402, 483–487 (1999)
Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)
Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y., Barkai, N.: Revealing modular organization in the yeast transcriptional network. Nature Genetics 31, 370–377 (2002)
Bergmann, S., Ihmels, J., Barkai, N.: Iterative signature algorithm for the analysis of large-scale gene expression data. Physical Review 67 (2003)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, Menlo Park (1996)
Becquet, C., Blachon, S., Jeudy, B., Boulicaut, J.F., Gandrillon, O.: Strongassociation- rule mining for large-scale gene-expression data analysis: a case study on human sage data. Genome Biology 12 (2002)
Creighton, C., Hanash, S.: Mining gene expression databases for association rules. Bioinformatics 19, 79–86 (2003)
Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered sets, pp. 445–470. Reidel, Dordrecht (1982)
Rioult, F., Boulicaut, J.F., Crémilleux, B., Besson, J.: Using transposition for pattern discovery from microarray data. In: Proceedings ACM SIGMOD Workshop DMKD 2003, San Diego, USA, pp. 73–79 (2003)
Rioult, F., Robardet, C., Blachon, S., Crémilleux, B., Gandrillon, O., Boulicaut, J.F.: Mining concepts from large sage gene expression matrices. In: Proceedings KDID 2003 co-located with ECML-PKDD 2003, Catvat-Dubrovnik, Croatia, pp. 107–118 (2003)
Boulicaut, J.F., Klemettinen, M., Mannila, H.: Modeling KDD processes within the inductive database framework. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 293–302. Springer, Heidelberg (1999)
De Raedt, L.: A perspective on inductive databases. SIGKDD Explorations 4, 69–77 (2003)
Pensa, R.G., Leschi, C., Besson, J., Boulicaut, J.F.: Assessment of discretization techniques for relevant pattern discovery from gene expression data. In: Proceedings BIOKDD 2004 co-located with SIGKDD 2004, Seattle, USA (2004) (to appear)
Besson, J., Robardet, C., Boulicaut, J.F.: Constraint-based mining of formal concepts in transactional data. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 615–624. Springer, Heidelberg (2004)
Robardet, C., Pensa, R., Besson, J., Boulicaut, J.F.: Using classification and visualization on pattern databases for gene expression data analysis. In: Proceedings PaRMa 2004 co-located with EDBT 2004, Heraclion-Crete, Greece. CEUR Proceedings, vol. 96, pp. 107–118 (2004)
Ashburnerand, M., Ball, C., Blake, J., Botstein, D., et al.: Gene ontology: tool for the unification of biology. the gene ontology consortium. Nature Genetics 25, 25–29 (2000)
Arbeitman, M., Furlong, E., Imam, F., Johnson, E., Null, B., Baker, B., Krasnow, M., Scott, M., Davis, R., White, K.: Gene expression during the life cycle of drosophila melanogaster. Science 297, 2270–2275 (2002)
Rome, S., Clément, K., Rabasa-Lhoret, R., Loizon, E., Poitou, C., Barsh, G.S., Riou, J.P., Laville, M., Vidal, H.: Microarray profiling of human skeletal muscle reveals that insulin regulates 800 genes during an hyperinsulinemic clamp. Journal of Biological Chemistry 278(20), 18063–18068 (2003)
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
Pensa, R.G., Besson, J., Boulicaut, JF. (2004). A Methodology for Biologically Relevant Pattern Discovery from Gene Expression Data. In: Suzuki, E., Arikawa, S. (eds) Discovery Science. DS 2004. Lecture Notes in Computer Science(), vol 3245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30214-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-30214-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23357-2
Online ISBN: 978-3-540-30214-8
eBook Packages: Springer Book Archive