A large number of clustering approaches have been proposed for the analysis of gene
expression data obtained from microarray experiments. However, the results from the …

Network inference, which is the reconstruction of biological networks from high-throughput
data, can provide valuable information about the regulation of gene expression in cells …

A critical step in the analysis of large genome-wide gene expression datasets is the use of
module detection methods to group genes into co-expression modules. Because of …

Cluster analysis is the automated search for groups of related observations in a dataset.
Most clustering done in practice is based largely on heuristic but intuitively reasonable …

We present a penalized matrix decomposition (PMD), a new framework for computing a rank-
K approximation for a matrix. We approximate the matrix X as, where dk, uk, and vk minimize …

Not so long ago, multivariate analysis consisted solely of linear methods illustrated on small
to medium-sized data sets. Moreover, statistical computing meant primarily batch processing …

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 …

DNA microarray technology has now made it possible to simultaneously monitor the
expression levels of thousands of genes during important biological processes and across …

DNA microarrays are a new and promising biotechnology which allows the monitoring of
expression levels in cells for thousands of genes simultaneously. The present paper …

Global analyses of RNA expression levels are useful for classifying genes and overall
phenotypes. Often these classification problems are linked, and one wants to find “marker …