DNA microarray technology has now made it possible to monitor the expression levels of thousands of genes simultaneously during important biological processes and across collections of related samples. Usually, gene expression matrix has several particular macroscopic phenotypes of samples. However, this matrix has few samples, and vast amounts of genes. This feature makes it difficult to class...

Microarray is used to generate large amount of gene expression data and observing the differences among gene expression levels. Gene expression time series data represents the trend of gene behaviors. Clustering is a popular analysis for gene expression time series data. Genes in the same cluster have similar behavior. Cluster analysis helps people investigate the relativity among genes. We pro...

Massive amounts of gene expression data are generated using microarrays for functional studies of genes and gene expression data clustering is a useful tool for studying the functional relationship among genes in a biological process. We have developed a computer package EXCAVATOR for clustering gene expression profiles based on our new framework for representing gene expression data as a minim...

Gene expression microarray data often include multiple missing values. Most gene expression analysis (including gene clustering analysis); however, require a complete data matric as an input. In ordinary clustering methods, just a single missing value makes one abandon the whole data of a gene even if the rest of data for that gene was intact. The quality of analysis may decrease seriously as t...

Gene clusters could be derived based on expression profiles, function categorization and promoter regions. To obtain thorough understanding of gene expression and regulation, the three aspects should be combined in an organic way. In this study, we explored the possible ways to analyze the large-scale gene expression data. Three approaches were used to analyze yeast temporal expression data: 1)...

MOTIVATION Various clustering methods have been applied to microarray gene expression data for identifying genes with similar expression profiles. As the biological annotation data accumulated, more and more genes have been organized into functional categories. Functionally related genes may be regulated by common cellular signals, thus likely to be co-expressed. Consequently, utilizing the rap...

MOTIVATION A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering gene expression data into homogeneous groups was shown to be instrumental in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on clustering algorit...

Massive amounts of gene expression data are generated using microarrays for functional studies of genes and gene expression data clustering is a useful tool for studying the functional relationship among genes in a biological process. We have developed a computer package EXCAVATOR for clustering gene expression pro®les based on our new framework for representing gene expression data as a minimu...

Motivation: A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering gene expression data into homogeneous groups was shown to be instrumental in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on clustering algori...

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 gene expression implies some form of regulatory or functional similarity. We give an overview of various clustering techniques, including conventional clustering methods (such as hierarchical clustering, k-means c...