Bioinformatics
People
Course director
Description
COURSE OBJECTIVES
Knowledge of basic principles of genome analysis Knowledge of basic principles of protein sequence analysis Building of 3D structures of proteins Docking simulations of drug binding to molecular targets.
COURSE DESCRIPTION
We deal with the most commonly used algorithms in biological applications. The first part of the course is dedicated to data mining algorithms and other classification methods such as markov models and other learning methods. We explore how to use such algorithms to analyse large amount of data (e.g., genome analysis) and to build 3D structures of proteins (e.g., using homology modeling). The last part of the course is focused on molecular docking algorithms and high throughput screening protocols used in virtual screening calculations.
LEARNING METHODS
Problem-Based Learning and Team-Based Learning Teaching method: frontal lectures + tutorial sessions
EXAMINATION INFORMATION
The course will be evaluated through assignments and a final project
REFERENCES
Notes and slides from the course
Suggested (not mandatory) book:
Essential Bioinformatics by Jin Xiong
Education
- Master of Science in Artificial Intelligence, Elective course, Lecture, 1st year
- Master of Science in Artificial Intelligence, Elective course, Lecture, 2nd year
- Master of Science in Computational Science, Elective course, 1st year
- Master of Science in Computational Science, Foundation course, 2nd year
- PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (4.0 ECTS)
- PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (4.0 ECTS)