Abstract
The availability of microarray technology at an affordable price makes it possible to determine expression of several thousand genes simultaneously. Gene expression can be clustered so as to infer the regulatory modules and functionality of a gene relative to one or more of the annotated genes of the same cluster. The outcome of clustering depends on the clustering method and the metric being used to measure the distance. In this paper we study the popular hierarchal clustering algorithm and verify how many of the genes in the same cluster share functionality. Further, we will also look into the supervised clustering method for satisfying hypotheses and view how many of these genes are functionally related.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mount, D.W.: Bioinformatics: sequence and genome analysis, 2nd edn., vol. xii, p. 692. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y (2004)
Jensen, L.J., Knudsen, S.: Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation. Bioinformatics 16(4), 326–333 (2000)
Draghici, S.: Data Analysis Tools for DNA Microarrays. Mathematical Biology and Medcine Series. Chapman & Hall/CRC (2003)
Xu, H.C.S., McCauley, E., Coombes, K., Xiao, L., Fischer, S.M., Clifford, J.L.: Chemoprevention of skin carcinogenesis by phenylretinamides: retinoid receptor independent tumor suppression. Clinical Cancer Research 12(3) (2006)
DiGiovanni, J.: Multistage carcinogenesis in mouse skin. Pharmacol Ther. 54(1), 63–128 (1992)
Verma, A.K.: Inhibition of both stage I and stage II mouse skin tumour promotion by retinoic acid and the dependence of inhibition of tumor promotion on the duration of retinoic acid treatment. Cancer Research 47, 5097–5101 (1987)
go-ontology, the gene ontology, http://www.geneontology.org/
Draghici, S., et al.: Global functional profiling of gene expression. Genomics, 98–104 (2003)
Eisen, M.B.: Gene Cluster. p. Hierarchical clustering, self-organizing maps (SOMs), k-means clustering, principal component analysis, http://rana.lbl.gov/EisenSoftware.htm
Seo, J., Gordish-Dressman, H., Hoffman, E.P.: An interactive power analysis tool for microarray hypothesis testing and generation. Bioinformatics (in press, 2006)
Bolshakova, N., Azuaje, F., Cunningham, P.: An integrated tool for microarray data clustering and cluster validity assessment. In: Proceedings of the 2004 ACM Symposium on Applied Computing (SAC). ACM Press, New York (2004)
Speer, N., Spieth, C., Zell, A.: A Memetic Clustering Algorithm for the Functional Partition of Genes Based on the Gene Ontology. In: Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2004). IEEE Press, Los Alamitos (2004)
Christian Spieth, F.S., Speer, N., Zell, A.: Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks (2005)
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
Loganantharaj, R., Cheepala, S., Clifford, J. (2006). On Clustering of Genes. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_104
Download citation
DOI: https://doi.org/10.1007/11779568_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
eBook Packages: Computer ScienceComputer Science (R0)