[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

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