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.

Evaluation

Belongs to following modules

Advanced studies

BIOADVC Compulsory advanced studies 68–80 ECTS

BIO2200 Introduction to computational biology, 3 ECTS

BIO4440 Biological data analysis with R, 5 ECTS

BIO4450 High-throughput data analysis, 5 ECTS

BIO4470 Systems biology methods, 5 ECTS

BIO4380 Bioinformatics project, 3–6 ECTS

BIOADVM Methodology studies 5–17 ECTS

5–17 ECTS

BIO4420 Programming and algorithms for bioinformatics, 5 ECTS

BIO4712 SGN-84007 Introduction to Matlab, 1 ECTS

BIO4722 Machine Learning, 5 ECTS

BIO4723 Data Structures and Algorithms, 5 ECTS

BIO4708 SGN-41006 Signal Interpretation Methods, 4 ECTS

BIO4715 TIE-02206 Basic Course on Programming, 5 ECTS

MTTTS12 Introduction to Bayesian Analysis 1, 5 ECTS

MTTTS14 Statistical Modeling 1, 5 ECTS

BIO4721 Introduction to Pattern Recognition and Machine Learning, 5 ECTS

BIO4030 M.Sc. Thesis seminar, 2 ECTS

BIO4031 M.Sc. Thesis, 40 ECTS

BIOADVE Elective advanced studies 10–20 ECTS

10–20 ECTS

Any course from BIOADVM Methodology studies can be included here.

BIO2085 Bioinformatics tools, 3 ECTS

BIO2450 Advanced bioinformatics tools, 5 ECTS

BIO4395 Bioinformatics work course, 5 ECTS

BIO4460 Journal club, 2 ECTS

BIO4035 Research work, 5–8 ECTS

BIO4230 Phylogenetics, 4 ECTS

BIO4240 Structural bioinformatics, 3–6 ECTS

BIO4716 SGN-53007 Computational Diagnostics, 5 ECTS

BIO4704 SGN-52406 Models of Gene Networks, 3 ECTS

BIO4705 SGN-52606 Processing of Biosignals, 5 ECTS

BIO4707 SGN-53206 Cell Culturing, Microscopy and Cell Image Analysis, 3 ECTS