Abstract
In this paper, we introduce the notion of dense regions in DNA microarray data and present algorithms for discovering them. We demonstrate that dense regions are of statistical and biological significance through experiments. A dataset containing gene expression levels of 23 primate brain samples is employed to test our algorithms. Subsets of potential genes distinguishing between species and a subset of samples with potential abnormalities are identified.
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
Cáceres, M., et al.: Elevated gene expression levels distinguish human from non-human primate brains. PNAS 100, 13030–13035 (2003)
Dennis, G., et al.: DAVID: database for annotation, visualization, and integrated discovery. Genome Biology 4(5), 3 (2003)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. PNAS 85, 14863–14868 (1998)
Lee, M.T.: Analysis of microarray gene expression data. Kluwer Academic Publishers, Dordrecht (2004)
Li, C., Wong, W.H.: Model-based analysis of oligonecleotide arrays: expression index computation and outlier detection. PNAS 36, 31–36 (2001)
Yang, C., Fayyad, U., Bradley, P.S.: Efficient discovery of error-tolerant frequent itemsets in high dimensions. In: Proc. of ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, San Francisco, California, pp. 194–203 (2001)
Yip, M., Wu, E.H., Ng, M.K., Chan, T.F.: An efficient algorithm for dense regions discovery from large-scale data streams. In: Proc. of 8th Pacific-Asia Conf. on on Knowledge Discovery and Data Mining: Sydney, Australia, pp. 116–120 (2004); an extended version availiable at: UCLA CAM Reports 03-76, Math. Dept., University of California, Los Angeles, CA (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
Yip, A.M., Wu, E.H., Ng, M.K., Chan, T.F. (2004). Unsupervised Dense Regions Discovery in DNA Microarray Data. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-28651-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
eBook Packages: Springer Book Archive