The latest advances in high-throughput techniques during the past decade allowed the
systems biology field to expand significantly. Today, the focus of biologists has shifted from …

Biclustering is a powerful data mining technique that allows clustering of rows and columns,
simultaneously, in a matrix-format data set. It was first applied to gene expression data in …

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It
is emerging as a standard tool for extracting knowledge from gene expression …

Background Biclustering techniques are capable of simultaneously clustering rows and
columns of a data matrix. These techniques became very popular for the analysis of gene …

Biclustering extends the traditional clustering techniques by attempting to find (all)
subgroups of genes with similar expression patterns under to-be-identified subsets of …

Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means
partitioning being the most popular methods. Numerous improvements of these two …

Background The learning of global genetic regulatory networks from expression data is a
severely under-constrained problem that is aided by reducing the dimensionality of the …

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms
with extensive real-life applications in data mining and bioinformatics. The authors first offer …

Background Biclustering of gene expression data searches for local patterns of gene
expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression …

DNA microarray technologies are used extensively to profile the expression levels of
thousands of genes under various conditions, yielding extremely large data-matrices. Thus …