Findings from clinical and biological studies are often not reproducible when tested in
independent cohorts. Due to the testing of a large number of hypotheses and relatively small …

Background The analysis of large-scale gene expression data is a fundamental approach to
functional genomics and the identification of potential drug targets. Results derived from …

The increasing use of high-throughput gene expression quantification technologies over the
last two decades and the fact that most of the published studies are stored in public …

Over the last decade, genome-wide association studies (GWAS) have become the standard
tool for gene discovery in human disease research. While debate continues about how to …

Background When genomics researchers design a high-throughput study to test for
differential expression, some biological systems and research questions provide …

An increasing amount of microarray gene expression data sets is available through public
repositories. Their huge potential in making new findings is yet to be unlocked by making …

While meta-analysis provides a powerful tool for analyzing microarray experiments by
combining data from multiple studies, it presents unique computational challenges. The …

A major contributor to the scientific reproducibility crisis has been that the results from
homogeneous, single-center studies do not generalize to heterogeneous, real world …

Large-scale transcriptome studies with multiple samples per individual are widely used to
study disease biology. Yet, current methods for differential expression are inadequate for …

Motivation: With the increasing availability of cancer microarray data sets there is a growing
need for integrative computational methods that evaluate multiple independent microarray …