Today, scientific discovery is increasingly data driven and requires computational support. However, there is an important class of problems for which purely automatic approaches do not suffice, since they require human reasoning and decision making and can benefit from contextual knowledge humans possess. In my talk I will show how to support this interplay between data, computation, visualization and humans.

I will give examples from molecular biology, specifically cancer subtype analysis and multivariate biological networks, but also introduce broadly applicable visual analysis methods for the analysis of multivariate ranking data and set-based data.

For an overview of the techniques and tools refer to http://caleydo.org and http://vcg.github.io/upset/.