Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data.

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently.

Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis.

Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this aggregation. Previous studies were limited to few data sets with a small number of patients.

A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer.

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation.

Diversity in degrees of freedom of mitochondrial transit peptides.

Most mitochondrial proteins are synthesized in the cytosol of eukaryotic cells as precursor proteins carrying N-terminal extensions called transit peptides or presequences, which mediate their specific transport into mitochondria. However, plant cells possess a second potential target organelle for such transit peptides, the chloroplast. It can therefore be assumed that mitochondrial transit peptides in plants are exposed to an increased demand of specificity, which in turn leads to reduced degrees of freedom in these transit peptides compared with those of nonplant organisms.