Abstract
One of the 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. It has been validated on real data sets. 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. Doing so, we survey our algorithmic breakthrough which has been the core of our contribution to the IST FET cInQ project.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
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)
Velculescu, V., Zhang, L., Vogelstein, B., Kinzler, K.: Serial analysis of gene expression. Science 270, 484–487 (1995)
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)
Robardet, C., Feschet, F.: Efficient local search in conceptual clustering. In: Jantke, K.P., Shinohara, A. (eds.) DS 2001. LNCS (LNAI), vol. 2226, pp. 323–335. Springer, Heidelberg (2001)
Dhillon, I., Mallela, S., Modha, D.: Information-theoretic co-clustering. In: Proceedings ACM SIGKDD 2003, pp. 1–10. ACM, New York (2003)
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)
Becquet, C., Blachon, S., Jeudy, B., Boulicaut, J.F., Gandrillon, O.: Strong association rule mining for large gene expression data analysis: a case study on human SAGE data. Genome Biology 12 (2002), See, http://genomebiology.com/2002/3/12/research/0067
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)
Besson, J., Robardet, C., Boulicaut, J.F., Rome, S.: Constraint-based concept mining and its application to microarray data analysis. Intelligent Data Analysis journal 9, 59–82 (2005)
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., Leschi, C., Besson, J., Boulicaut, J.F.: Assessment of discretization techniques for relevant pattern discovery from gene expression data. In: Proceedings 4th ACM SIGKDD Workshop BIOKDD 2004, Seattle, USA, pp. 24–30. ACM, New York (2004)
Boulicaut, J.F., Bykowski, A., Rigotti, C.: Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery journal 7, 5–22 (2003)
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)
Pensa, R., Besson, J., Boulicaut, J.F.: A methodology for biologically relevant pattern discovery from gene expression data. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 230–241. 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 Workshop Proceedings, vol. 96 (2004)
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)
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)
Goethals, B., Zaki, M.: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations FIMI 2003, Melbourne, USA (2003)
Stumme, G., Taouil, R., Bastide, Y., Pasqier, N., Lakhal, L.: Computing iceberg concept lattices with TITANIC. Data & Knowledge Engineering 42, 189–222 (2002)
Lash, A., Tolstoshev, C., Wagner, L., Schuler, G., Strausberg, R., Riggins, G., Altschul, S.: SAGEmap: A public gene expression resource. Genome Research 10, 1051–1060 (2000)
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
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pensa, R.G., Besson, J., Robardet, C., Boulicaut, JF. (2006). Contribution to Gene Expression Data Analysis by Means of Set Pattern Mining. In: Boulicaut, JF., De Raedt, L., Mannila, H. (eds) Constraint-Based Mining and Inductive Databases. Lecture Notes in Computer Science(), vol 3848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11615576_15
Download citation
DOI: https://doi.org/10.1007/11615576_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31331-1
Online ISBN: 978-3-540-31351-9
eBook Packages: Computer ScienceComputer Science (R0)