Course organized by TUT, see TUT study guide for up-to-date information.
Learning outcomes
After completing the course, the student can: - gained a basic understanding of the definition and the meaning of computational diagnostics and its utility for biomedical research. - Case studies will be discussed illustrating the interplay between computational and statistical methods that are applied to large-scale and high-dimensional data sets from genomic and genetic experiments. - Moreover, the student will learn how to practically approach such problems by using the statistical programming language R. - In general, the course teaches statistical thinking in the context of biomedical problems, i.e., the adaptation of machine learning methods in a problem specific manner.
Contents
- Classification of disease groups - Biomarker identification - Survival analysis - Human disease network