▪ Abstract DNA microarrays have enabled biology researchers to conduct large-scale
quantitative experiments. This capacity has produced qualitative changes in the breadth of …

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 …

Time series data can now be routinely collected for biochemical reaction pathways, and
recently, several methods have been proposed to infer reaction mechanisms for metabolic …

Interest in microarrays has increased considerably in the last ten years. This increase in the
use of microarray technology has led to the need for good standards of microarray …

Cancer is a complex and heterogeneous disease that exhibits high levels of robustness
against various therapeutic interventions. It is a constellation of diverse and evolving …

In this paper, we propose a hybrid clustering method that combines the strengths of bottom-
up hierarchical clustering with that of top-down clustering. The first method is good at …

This chapter discusses the statistical analysis of microarray time course data, with a focus on
developmental time course experiments. The methods reviewed here are generally suitable …

The development of microarray technology has enabled scientists to measure the
expression of thousands of genes simultaneously, resulting in a surge of interest in several …

The regulation of pig theca cell steroidogenesis was studied by the development of a
physiological serum-free culture system, which was subsequently extended to investigate …

Principal component, cluster and discriminant analysis are multivariate statistical methods
that are widely used in archaeometry. They are examples of what are known in some …