Background Distance based unsupervised clustering of gene expression data is commonly
used to identify heterogeneity in biologic samples. However, high noise levels in gene …

Background Gene expression similarity measuring methods were developed and applied to
search rapidly growing public microarray databases. However, current expression similarity …

Various distance functions for evaluating the differences between gene expression profiles
have been proposed in the past. Such a function would output a low value if the profiles are …

Abnormal regulation of signaling pathways is the key causative factor in several diseases.
Although many methods have been proposed to identify significantly differential pathways …

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 …

Background Clustering is crucial for gene expression data analysis. As an unsupervised
exploratory procedure its results can help researchers to gain insights and formulate new …

Extracting biological insight from microarray data is important but challenging. Here we
describe TAPPA, a java-based tool, for identification of phenotype-associated genetic …

Motivation: Because co-expressed genes are likely to share the same biological function,
cluster analysis of gene expression profiles has been applied for gene function discovery …

Background An important emerging trend in the analysis of microarray data is to incorporate
known pathway information a priori. Expression level" summaries" for pathways, obtained …

The concept of depth induces an ordering from centre outwards in multivariate data. Most
depth definitions are unfeasible for dimensions larger than three or four, but the Modified …