Identification of groups of functionally related genes from high throughput gene expression data is an important step towards elucidating gene functions at a global scale. Most existing approaches treat gene expression data as points in a metric space, and apply conventional clustering algorithms to identify sets of genes that are close to each other in the metric space. However, they usually i...

MOTIVATION Bi-clustering algorithms aim to identify sets of genes sharing similar expression patterns across a subset of conditions. However direct interpretation or prediction of gene regulatory mechanisms may be difficult as only gene expression data is used. Information about gene regulators may also be available, most commonly about which transcription factors may bind to the promoter regio...

Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. This paper proposes a suitable dissimilarity measure for gene expression time series data sets. It also presents a graph-based clustering method for findi...

Microarray data is gene expression data which consists of the protein level of various genes for some samples. It is a high dimensional data. High dimensionality is a curse for the analysis of gene expression data. Thus gene selection process is used in which most informative genes are selected from the pool of gene expression data set. All the genes are not relevant in each case. First we need...

Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data t...