SURF 64 aims to provide a comprehensive introduction to advanced statistical models to perform in-depth OMICs screening using established methods and their latest extensions, in order to allow the integration of several OMICs profiles measured in the same individuals.

Specifically, the course will develop the theoretical background of these methods, their applicability to OMICs data in epidemiology and Health Sciences, and will feature a large proportion of practical work using real data: 6 full days on a project-driven research exercise and 4 afternoons of practical work.

SURF 64 proposes to build upon a statistical background such as the one taught in the parent Stat-XP course (Imperial College) or the Molecular Epidemiology Course (Utrecht University), and to present necessary extensions of these methods in order to:

• Accommodate complex study designs
• Improve results interpretability
• Handle multiple sets of OMICs data

In addition to regression-based profiling approaches, SURF 64 will also feature detailed exploration of machine learning approaches, including network inference and their practical application to real data.

Overview

SURF 64 will enable research professionals with interest and experience in exploring OMICs data to enrich their methodological knowledge to more advanced techniques that are able to accommodate and exploit the OMICs data complexity in real life settings. To ensure the direct applicability of the methods, the course features practical sessions as well as group work as mini projects using real multi-OMIC data sets.

Fees

Early bird registration (until May 31st 2017)

• Academic: £1,900
• Non-academic: £2,500

Standard registration

• Academic: £2,300
• Non-academic: £2,800