High throughput gene expression analysis is becoming more and more important in many
areas of biomedical research. cDNA microarray technology is one very promising approach …

The analysis of microarray data is essential for large amounts of gene expression data. In
this review we focus on clustering techniques. The biological rationale for this approach is …

Clustering is one of the major exploratory techniques for gene expression data analysis.
Only with suitable similarity metrics and when datasets are properly preprocessed, can …

DNA microarray technology has resulted in the generation of large complex data sets, such
that the bottleneck in biological investigation has shifted from data generation, to data …

Motivation: Clustering is one of the most widely used methods in unsupervised gene
expression data analysis. The use of different clustering algorithms or different parameters …

A versatile, platform independent and easy to use Java suite for large-scale gene
expression analysis was developed. Genesis integrates various tools for microarray data …

Within the field of genomics, microarray technologies have become a powerful technique for
simultaneously monitoring the expression patterns of thousands of genes under different …

Abstract Analysis of large-scale gene expression studies usually begins with gene
clustering. A ubiquitous problem is that different algorithms applied to the same data …

Clustering genes into biological meaningful groups according to their pattern of expression
is a main technique of microarray data analysis, based on the assumption that similarity in …

Conclusions This chapter has introduced key aspects of clustering systems for genomic
expression data. An overview of the major types of clustering approaches, problems and …