Learning outcomes

After the module, the student can:
- explain bioinformatics methods and their significance in different applications.
- use methods of bioinformatics to solve biomedical research problems.
- compare methods in bioinformatics to select a suitable approach for a specified task.
- apply Python and R programming to implement the necessary analysis.
- plan and manage independent work and report the results.

Belongs to following modules

Compulsory advanced studies

BIO2200 Introduction to computational biology, 3 ECTS (1.aut)

BIO4440 Biological data analysis with R, 5 ECTS (1.spr)

BIO4450 High-throughput data analysis, 5 ECTS (1.spr)

BIO4470 Systems biology methods, 5 ECTS (2.aut)

BIO4380 Bioinformatics project, 3–6 ECTS (2.aut)

BIOADVM Methodology studies 5–17 ECTS

5-17 ECTs to be completed

BIO4420 Programming and algorithms for bioinformatics, 5 ECTS (1.spr)

BIO4723 Data Structures and Algorithms, 5 ECTS

BIO4715 TIE-02206 Basic Course on Programming, 5 ECTS

BIO4721 Introduction to Pattern Recognition and Machine Learning, 5 ECTS

BIO4722 SGN-41007 Pattern Recognition and Machine Learning, 5 ECTS

BIO4712 SGN-84007 Introduction to Matlab, 1 ECTS

MTTTS12 Introduction to Bayesian Analysis 1, 5 ECTS

MTTTS14 Statistical Modeling 1, 5 ECTS

BTK4030 Thesis Seminar, 2 ECTS

BTK4031 M.Sc. Thesis, 40 ECTS