PROCEEDINGS IPMU '08
Max-variance Clustering and Biclustering of Microarray Data
C. Cano, S. Blanco, F. García, A. Blanco
Microarray technology allows to
measure the expression of thousands
of genes simultaneously, and
under tens of specific conditions.
Clustering and Biclustering are the
main tools to analyze gene expression
data, since they reveal genes
with the same behavior across samples.
In this paper we present
three novel approaches for Clustering
and Biclustering based on Estimation
of Distribution Algorithms
(EDA) and Principal Components
Analysis. The goal is to find nonexclusive
(potentially overlapping)
groups of genes with similar behavior
and maximum between-sample
variance. We tested the proposed
methods on two real datasets, outperforming
previous results in terms
of quality and size of revealed patterns.
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