Dr. Clemens Kreutz

In Bioinformatics and Systems Biology, a huge variety of computational tools and statistical approaches have been developed. However, many computational methods are not well-tested in application settings and their applicability is often seriously delimited. Therefore, selecting an optimal analysis strategy of often difficult in applications and missing guidelines hamper the transfer of theoretical approaches to experimental research.

In this talk, new concepts for assessing statistical algorithms will be introduced and illustrated. The suggested methodology enables less biased, more reliable and valid comparisons of competing approaches than currently performed in the literature. The presented concepts can be applied to establish optimized analysis pipelines and for developing general decision guidelines for the selection of appropriate analysis methods. Thereby, the presented methodology constitutes a promising perspective for transferring computational approaches to basic research in academia and to industrial applications like drug development.