Professor of BioInformatics
School of Bioscience
Choose a course instance to see course syllabus and admission requirements.
Application is done after nomination.
A continuing challenge within bioinformatics is to integrate and analyze large amounts of data from different large-scale experiments (for example proteomics, genomics and metabolomics). This course introduces the programming language R to efficiently solve various bioinformatics problems. You will learn the basics in R such as data types, functions, statistics and visualization, and in an advanced way apply the knowledge on various types of data. We examine how the experimental design can influence results and introduce various normalization methods. Within bioinformatics, R can be applied in many ways from identification of new cancer specific genes to analyze metabolite changes caused by drug treatment.