Single-Course English 5 ECTS

Computational Precision Medicine

Overall Course Objectives

The aim of this course is to enable the students to apply a range of computational tools for precision medicine. In this course, we emphasize topics in precision diagnostics, such as patient stratification using gene expression profiling, as well as precision therapeutics, with a focus on identification and evaluation of targets for cancer immunotherapy.

Learning Objectives

  • explain the concepts of applied precision medicine in the clinic.
  • explain the concepts of treatment stratification of cancer patients.
  • explain the concepts of cancer immunotherapy.
  • apply basic methods for unsupervised clustering based on gene expression.
  • apply basic methods for sample classification based on gene expression.
  • apply differential gene expression analysis to find potential tumor associated antigens.
  • apply isoform switch analysis to find potential surface antigen targets.
  • analyze the utility of computational precision medicine in relation to their application in the clinic.

Course Content

In this course, the students will be introduced to bioinformatics methods in precision medicine. The students will learn how transcriptomics data from microarrays or RNA sequencing can be used for diagnostics and patient stratification for selecting optimal treatments. The students will also learn the principles of precision and personalized therapeutics by analyzing RNA and DNA sequencing data.

Each lecture consists of an introduction to a clinical problem by a domain expert from translational research and/or a clinician, followed by an introduction to how to solve the problem bioinformatically, followed by hands on exercises. The latter two will be based on the programming language R.

The evaluation is based on hands on group projects, in which a precision medicine problem is solved. The students can either choose a predefined project or come up with their own. The examination consists of a group project presentation, followed by individual examinations in the full curriculum.

Recommended prerequisites

02402/22110/22100/22126/27002/27008, The course assumes that the student has basic experience in R programming, as all exercises and group assignments will be based on this language. A basic understanding of next generation sequencing and a good understanding of statistics are highly recommended.

Teaching Method

Lectures, computer exercises, and group work

Limited number of seats

Minimum: 10.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.

See course in the course database.





3 weeks


Health Tech


DTU Lyngby Campus

Course code 22123
Course type Candidate
Semester start Week 23
Semester end Week 26
Days Mon-fri 8:00-17:00

7.500,00 DKK

Please note that this course has participants limitation. Read more