Abstract
This paper addresses the problem of clustering gene expression profiles based on automatically extracted seeds which are obtained by our proposed method. Specifically, we introduce a new clustering methodology that consists of three stages: seed extraction, cluster generation, and its evaluation. Performance analysis of the proposed methodology is done with a real dataset, and its results are reported in detail. Overall, based on our empirical studies, the proposed clustering methodology seems to be very favorable for gene expression data analysis, as alternatives to current clustering methods.
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
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster Analysis and Display of Genome-Wide Expression Patterns. Proc. Natl. Acad. Sci. 95, 14863–14868 (1998)
Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic Determination of Genetic Network Architecture. Nature Genetics 22, 281–285 (1999)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R.: Interpreting Patterns of Gene Expression with Self-Organizing Maps: Methods and Application to Hematopoietic Differentiation. Proc. Natl. Acad. Sci. 96, 2907–2912 (1999)
Yeung, K.Y., Ruzzo, W.L.: Principle Component Analysis for Clustering Gene Expression Data. Bioinformatics 17(9), 763–774 (2001)
Golub, G.H., Van Loan, C.F.: Matrix Computation, 3rd edn. The Johns Hopkins University Press, Baltimore (1996)
Yeung, K.Y., Haynor, D.R., Ruzzo, W.L.: Validating Clustering for Gene Expression Data. Bioinformatics 17(4), 309–318 (2001)
Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T.G., Gabrielian, A.E., Landsman, D., Lockhart, D.J., Davis, R.W.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2, 65–73 (1998)
The dataset is available at: http://staff.washington.edu/kayee/cluster/
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
Shin, M., Park, SH. (2004). Cluster Analysis of Gene Expression Profiles Using Automatically Extracted Seeds. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-30134-9_36
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23205-6
Online ISBN: 978-3-540-30134-9
eBook Packages: Springer Book Archive