Stephan Struckmann

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Annotated phylogenetic trees that display the evolution of transcription factor binding in regulatory regions are useful for e.g. 1) narrowing down true positive predicted binding sites, providing predictions for binding sites that can be tested experimentally, and 2) giving insight into the evolution of gene regulation and regulatory networks. We describe(More)
In silico approaches are increasingly considered to improve breast cancer treatment. One of these treatments, neoadjuvant TFAC chemotherapy, is used in cases where application of preoperative systemic therapy is indicated. Estimating response to treatment allows or improves clinical decision-making and this, in turn, may be based on a good understanding of(More)
Experimentally validated data on gene regulation are hard to obtain. In particular, information about transcription factor binding sites in regulatory regions are scattered around in the literature. This impedes their systematic in-context analysis, e.g. the inference of their conservation in evolutionary history. We demonstrate the power of integrative(More)
The prediction of transcription factor binding sites is difficult for many reasons. Thus, filtering methods are needed to enrich for biologically relevant (true positive) matches in the large amount of computational predictions that are frequently generated from promoter sequences. ReXSpecies 2 filters predictions of transcription factor binding sites and(More)
To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current(More)
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