Module Number

Module Title

Computational Immunomics
Type of Module

Elective Compulsory
Work load
- Contact time
- Self study
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Exam, exercise performance can flow into the exam as bonus points

Lecture type(s) Lecture, Tutorial

This lecture provides an introduction to the world of immunoinformatics. It deals with the application of informatics methods to solve immunological problems, such as the development of new vaccines. Core topics include introduction to immunology, machine learning methods, MHC-peptide binding prediction, antigen processing prediction, vaccine design, and systems immunology.


Understanding in dealing with immunological data. Transfer of methodological competences (machine learning) to concrete biological applications (immunology). Ability to develop and use own tools for immunoinformatics in a team. Project work strengthens teamwork and presentation skills.

Allocation of credits / grading
Type of Class
Type of Exam
Exam duration
of Module (%)
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Kohlbacher

Vorlesungsfolien werden in der Vorlesung verteilt. Goldsby, Kindt, Osborne, Kuby: Immunology (5th ed.), Freeman, 2003; Murphy \& Weaver:
Janeway’s Immunobiology (9th ed.), Garland Science, 2016; Hastie, Tibshirani, Friedman: The Elements of Statistical Learning Springer, 2001; Christianini, Shawe-Taylor: An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge U Press, 2000; Lund, Nielsen, Lundegaard, Kesmir, Buus: Immunological Bioinformatics, MIT Press, 2005

Last offered Sommersemester 2020
Planned for Sommersemester 2024
Assigned Study Areas BIOINFM2210, BIOINFM2510, INFM2510, MDZINFM2510, MDZINFM3110, MEINFM3210