[Bioc-devel] [workshop announcement]: Workshop Statistical Learning of Biological Systems - Ascona, Switzerland, May 31 to June 5, 2015

[Wolfgang Huber]

[although this is an event announcement, I hope it is still within scope of what people on this list might want to receive - apologies if not or if you already saw this elsewhere]

Workshop on Statistical Learning of Biological Systems from Perturbations
in Ascona, Switzerland, May 31 to June 5, 2015

Submissions of contributed presentations are invited. More details and registration instructions are available at http://www.cbg.ethz.ch/news/ascona2015
Confirmed invited speakers include:

	• Brenda Andrews (Donnelly Centre),
	• Alexis Battle (Johns Hopkins University),
	• Roderick Beijersbergen (Netherlands Cancer Institute),
	• Michael Boutros (DKFZ, Heidelberg),
	• Anne Carpenter (Broad Institute),
	• Bernd Fischer (EMBL/DKFZ Heidelberg),
	• Susan Holmes, Stanford
	• Marloes Maathuis (ETH Zurich),
	• George Michailidis (University of Michigan),
	• Lars Steinmetz (EMBL Heidelberg and Stanford)

This interactive, discussion-friendly meeting is set in a beautiful villa in Ascona overlooking the lake Lago Maggiore on the Italian-Swiss border, at the foot of the Alps.

Advances in biotechnology have made genome-scale measurements routine, including most recent techniques for perturbing individual genes in a targeted manner. These interventional data hold the promise to infer biological networks and to move forward systems biological approaches significantly. A major challenge now is to use the vast amount of data generated from these technologies and to devise appropriate statistical models and computational inference methods. Unlike observational data, interventional data can reveal causal relationships among genes or other biomolecular entities. As such, the statistical analysis and computational integration of perturbation data is an important step towards large-scale biological system identification with abundant applications in biology and medicine. This workshop will (i) explore recent advances and open problems in statistical learning, data integration, and causal inference of biological systems; (ii) present biomedical applications to recent genome-wide perturbation data, such as RNA interference data, obtained, for example, from cancer cells or cells infected by pathogens; and (iii) facilitate meaningful interaction between biomedical and quantitative researchers.

Niko Beerenwinkel, Peter Buehlmann, Darlene Goldstein, Wolfgang Huber