Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal...

We propose here a simple and robust approach for meta-analysis of molecular association studies. Making use of the binary structure of the data, and by treating the genotypes as independent variables in a logistic regression, we apply a simple and commonly used methodology that performs...

This paper discusses characteristics of dye biases in microarray data that the conventional normalization methods do not handle, and proposes a new normalization method involving a mixture of splines model. We also develop a test for between-group comparisons of each gene that is designed to be...

The etiology of complex diseases is heterogeneous. The presence of risk alleles in one or more genetic loci affects the function of a variety of intermediate biological pathways, resulting in the overt expression of disease. Hence, there is an increasing focus on identifying the genetic basis of...

Statistical analysis of microarray gene expression data has recently attracted a great deal of attention. One problem of interest is to relate genes to survival outcomes of patients with the purpose of building regression models for the prediction of future patients' survival based on their gene...

Microarray applications for the study of gene expression are becoming accessible for researchers in more and more systems. Applications from field or laboratory experiments are often complicated by the need to superimpose sample pairing for two-color arrays on experimental designs that may...

A number of computational methods have been proposed for identifying transcription factor binding sites from a set of unaligned sequences that are thought to share the motif in question. We here introduce an algorithm, called cosmo, that allows this search to be supervised by specifying a set of...

In microarray experiments, several steps may cause sub-optimal quality and the need for quality control is strong. Often the experiments are complex, with several conditions studied simultaneously. A linear model for paired microarray experiments is proposed as a generalisation of the paired...

We outline a method of sample size calculation in microarray experiments on the basis of pilot data and illustrate its practical application with both simulated and real data. The method was shown to be consistent (as the number of `probed genes' tends to infinity) under general conditions in an...

Experiments to determine the complete 3-dimensional structures of protein complexes are difficult to perform and only a limited range of such structures are available.In contrast, large-scale screening experiments have identified thousands of pairwise interactions between proteins, but such...