The performance results of a wide range of different classifiers applied to proteomic mass spectra data, in a blind comparative assessment organised by Bart Mertens, are reviewed. The different approaches are summarised, issues of how to evaluate and compare the predictions are described, and...

Information obtained by microarray studies frequently cause discrepancies between projects, laboratories and/or even repeated experiments. Such inconsistencies may be due to the lack of generality in the intellectual frameworks that form the basis for understanding the data. This article...

This paper gives a description of the structure of the classification competition on mass spectrometry proteomic data which is presented in this issue. All steps according to which the competition was run, leading up to the joint meeting, are explained. The paper gives further details on the...

The level of differential gene expression may be defined as a fold change, a frequency of upregulation, or some other measure of the degree or extent of a difference in expression across groups of interest. On the basis of expression data for hundreds or thousands of genes, inferring which genes...

Gene microarray technology is often used to compare the expression of thousand of genes in two different cell lines. Typically, one does not expect measurable changes in transcription amounts for a large number of genes; furthermore, the noise level of array experiments is rather high in...

We develop an approach for microarray differential expression analysis, i.e. identifying genes whose expression levels differ between two or more groups. Current approaches to inference rely either on full parametric assumptions or on permutation-based techniques for sampling under the null...

Normalization of expression levels applied to microarray data can help in reducing measurement error. Different methods, including cyclic loess, quantile normalization and median or mean normalization, have been utilized to normalize microarray data. Although there is considerable literature...

Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g., the effects showing in gene expression profiles or as morphological...

Maximum Likelihood (ML) is used as a standard method for estimating divergence times in phylogenetic trees. The method is consistent and hence the precision can be improved by analyzing longer sequences. In this paper we show that the precision can be improved also by including more taxa to the...

Many clustering methods require that the number of clusters believed present in a given data set be specified a priori, and a number of methods for estimating the number of clusters have been developed. However, the selection of the number of clusters is well recognized as a difficult and open...