Topics in Computational Biology & Analysis of Scientific Data using R

Lecture room: GPS Google maps link, OG 1.


Conscious and well-considered handling of scientific projects by computational methods (primarily R based).


None - but some interest in theoretical work and the software supporting such type of work is necessary.
For practice at home a notebook or PC is recommended.


Register with this registration form by email.
Deadline of registration: date of the regular start of the lecture period.


The field of computational biology is mainly based on programming languages, algorithms and applied mathematics. In this course we focus on the mathematical language R and try to learn step by step the philosophy of theoretical work and the art of computing including algorithms and mathematics/statistics.
Because this course mostly faces the situation that beginners and experienced students / scientists come together, we start every semester with the basics and evolve to advanced projects over time.
The reason for offering such an interdisciplinary and theoretical course for advanced students and post graduate researchers at the medical faculty, but also open for the natural science faculties, is based on the raising complexity in the life sciences and the increased use of theoretical methods. To bridge the existing gap between medical sciences and this highly theoretical field, the course is based on interactivity and a course level adjusted to the needs of the participants. Therefore we also encourage people with research interests in the experimental life sciences to join this course even if they to not expect to work on this field, to improve their insight into the methods and philosophy of bioinformatics / systems biology.
The audience is also encouraged to make their own proposals.
As per request we might extend to other processing languages too.


A server with present and past course resources is available.


Prof. Korsching.


Elective course certificate.
It is based on one or two lecture(s). The live R projects, should not (entirely) being part of the seminar to that point. The R source file(s) + explanatory slides need to be send to the course instructor. Certificates will be available the week thereafter in the office of the institute by Sandra Holert. Attendance at the seminar/ practical: at least 90% of the double periods.


Extra curricular course

  • Course ID

  • Type
    Seminar / practical

  • Class Start
    Second week of lecture period

  • Timeline

  • Estimated Effort
    Seminar 2 hours per week +
    30 min. @home

  • Class Size
    Min. 4 - max. 10 people

  • Details
    Download syllabus

  • Open questions?
    By email

  • Registration?
    By form & email


This means we try to join learning and research as far as possible. We also encourage successful participants of the courses to consider projects in our field of research.