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Algorithms for bounded-error correlation of high dimensional data in microarray experiments

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3 Author(s)
Koyuturk, M. ; Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN, USA ; Grama, A. ; Szpankowski, W.

The problem of clustering continuous valued data has been well studied in literature. Its application to microarray analysis relies on such algorithms as k-means, dimensionality reduction techniques, and graph-based approaches for building dendrograms of sample data. In contrast, similar problems for discrete-attributed data are relatively unexplored. An instance of analysis of discrete-attributed data arises in detecting co-regulated samples in microarrays. In this paper, we present an algorithm and a software framework, PROXIMUS, for error-bounded clustering of high-dimensional discrete attributed datasets in the context of extracting co-regulated samples from microarray data. We show that PROXIMUS delivers outstanding performance in extracting accurate patterns of gene-expression.

Published in:

Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE

Date of Conference:

11-14 Aug. 2003

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