Summer School on Statistical Inference in Computational Biology

SICSA International Summer School on
Statistical Inference in Computational Biology
National E-Science Centre, Edinburgh, United Kingdom
14-18 June 2010

Terry Speed (University of California, Berkeley)
Michael Stumpf (Imperial College)
Dirk Husmeier (Biomathematics and Statistics Scotland)
Chris Holmes (University of Oxford)
Manfred Opper (Technical University of Berlin)


Technological advances in the life sciences are producing vast amounts of data describing organisms at all levels of organisation. The impact of this on Informatics and the Computational Sciences has been enormous: the new disciplines of computational biology and bioinformatics were born to organise and model these data, and are now some of the fastest growing and most exciting areas in computer science. The increasing awareness of the noisy and incomplete nature of most biological data has led to a widespread use of statistical and machine learning tools within the field.

The school will focus on the role of statistical inference in biological modelling, with a particular emphasis on the Bayesian framework. It is mostly aimed at PhD students in computational subjects or quantitative biology, although early career researchers wishing to acquire more statistical modelling skills are also welcome. The school will consist of 6 4-hour modules, each delivered by an expert of international standing over 5 days. The first two sessions will serve as an introduction to multi-variate and Bayesian statistics respectively with a leaning towards the tools required in Computational Biology. The remaining sessions will cover four of the main inference tasks in Computational Biology – network reconstruction, inference within models of biological processes, inference in phylogenetics and phenotype-genotype associations to explain genetic diseases.

For more information, email sicb(at)

Organisers: Guido Sanguinetti (University of Edinburgh) Simon Rogers (University of Glasgow)