Abstract
We analyse data from the Edinburgh Mouse Atlas Gene-Expression Database (EMAGE) which is a high quality data source for spatio-temporal gene expression patterns. Using a novel process whereby generated patterns are used to probe spatially-mapped gene expression domains, we are able to get unbiased results as opposed to using annotations based predefined anatomy regions. We describe two processes to form association rules based on spatial configurations, one that associates spatial regions, the other associates 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
Creighton, C., Hanash, S.: Mining gene expression databases for association rules. Bioinformatics 19(1), 79–86 (2003)
Becquet, C., Blachon, S., Jeudy, B., Boulicaut, J.F., Gandrillon, O.: Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human sage data. Genome Biology 3(12) (2002)
Tešić, J., Newsam, S., Manjunath, B.S.: Mining image datasets using perceptual association rules. In: Proceedings of SIAM Sixth Workshop on Mining Scientific and Engineering Datasets in conjunction with the Third SIAM International Conference (2003)
Rushing, J.A., Ranganath, H.S., Hinke, T.H., Graves, S.J.: Using association rules as texture features. IEEE Transactions on Pattern Analyses and Machine Intelligence (2001)
Bevk, M., Kononenko, I.: Towards symbolic mining of images with association rules: Preliminary results on textures. Intelligent Data Analysis 10(4), 379–393 (2006)
Malik, H.H., Kender, J.R.: Clustering web images using association rules, interestingness measures, and hypergraph partitions. In: ICWE ’06: Proceedings of the 6th international conference on Web engineering, Palo Alto, California, USA, pp. 48–55. ACM Press, New York (2006), doi:10.1145/1145581.1145591
Ordonez, C., Omiecinski, E.: Discovering association rules based on image content. In: Proceedings of the IEEE Advances in Digital Libraries Conference (ADL’99), Baltimore, Maryland, IEEE Computer Society Press, Los Alamitos (1999), citeseer.ist.psu.edu/ordonez99discovering.html
Christiansen, J.H., Yang, Y., Venkataraman, S., Richardson, L., Stevenson, P., Burton, N., Baldock, R.A., Davidson, D.R.: Emage: a spatial database of gene expression patterns during mouse embryo development. Nucleic Acids Research 34, 637–641 (2006)
Gray, P.A., et al.: Mouse brain organization revealed through direct genome-scale tf expression analysis. Science 306(5705), 2255–2257 (2004)
Theiler, K.: The House Mouse Atlas of Embryonic Development. Springer, New York (1989)
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp. 207–216. ACM Press, New York (1993), citeseer.ist.psu.edu/agrawal93mining.html
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Peckham, J. (ed.) Proceedings ACM SIGMOD International Conference on Management of Data, pp. 255–264. ACM Press, New York (1997)
Jaccard, P.: The distribution of flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912)
Pallier, C., Scaffidi, P., Chopineau-Proust, S., Agresti, A., Nordmann, P., Bianchi, M.E., Marechal, V.: Association of chromatin proteins high mobility group box (hmgb) 1 and hmgb2 with mitotic chromosomes. Mol. Biol. Cell 14(8), 3414–3426 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
van Hemert, J., Baldock, R. (2007). Mining Spatial Gene Expression Data for Association Rules. In: Hochreiter, S., Wagner, R. (eds) Bioinformatics Research and Development. BIRD 2007. Lecture Notes in Computer Science(), vol 4414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71233-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-71233-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71232-9
Online ISBN: 978-3-540-71233-6
eBook Packages: Computer ScienceComputer Science (R0)