Interactive Gene Clustering—A Case Study of Breast Cancer Microarray Data

Alicja Gruźdź, Aleksandra Ihnatowicz and Dominik Ślʁzak
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Alicja Gruźdź: University of Regina
Aleksandra Ihnatowicz: University of Regina
Dominik Ślʁzak: University of Regina

Information Systems Frontiers, 2006, vol. 8, issue 1, No 3, 27 pages

Abstract: Abstract We present a new approach to clustering and visualization of the DNA microarray gene expression data. We utilize the self-organizing map (SOM) framework for handling (dis)similarities between genes in terms of their expression characteristics. We rely on appropriately defined distances between ranked genes-attributes, also capable of handling missing values. As a case study, we consider breast cancer data and the gene ESR1, whose expression alterations, appearing for many of the tumor subtypes, have been already observed to be correlated with some other significant genes. Preliminary results positively verify applicability of our approach, although further development is definitely needed. They suggest that it may be very effective when used by the domain experts. The algorithmic toolkit is enriched with GUI enabling the users to interactively support the SOM optimization process. Its effectiveness is achieved by drag&drop techniques allowing for the cluster modification according to the expert knowledge or intuition.

Keywords: DNA microarrays; Breast cancer; Self-organizing maps; Missing values; Entropy (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-005-6100-x

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