Biomedical Methods in Life Science

Statistical, machine learning, and bioinformatics tools have become fundamental in most biological, medical, and biotechnological applications. With biological data being generated at a continuously increasing pace, it is now possible to develop algorithms that can tackle complex problems, such as the analysis of metagenomics samples, or the prediction of protein structure and function using deep learning.
DTU has a consolidated experience in developing bioinformatics algorithms, several of which are used worldwide and have accumulated thousands of citations. This study line will provide the competences to:

Understand a wide range of biological problems
Have an insight in the available experimental data
Master statistical, bioinformatics, and machine learning algorithms to create effective models
Understand and use data science principles to analyse large datasets and the results of complex algorithms

General Competence Courses

A total of 30 ETCS must be completed in General Competence Courses. General Competence Courses are divided into three groups: GR1 consists of 15 ECTS mandatory courses that teaches essential skills relating to the specific field of this programme, GR2 courses relate to innovation, entrepreneurship & management and GR3 are optional general competences. The general competence courses are listed in the curriculum.

Tecnological Specialisation Courses

TS1 - Technological Specialisation Courses recommended for the study line of Bioinformatic methods in life science:

02450	Introduction to Machine Learning and Data Mining	5	point	Spring F4A (Tues 13-17), Autumn E4A (Tues 13-17)
22125	Algorithms in bioinformatics	5	point	June

TS2 - Other Technological Specialisation Courses in the study line of Bioinformatics methods in Life Science may include the following:

02456	Deep learning	5	point	Autumn E2A (Mon 13-17)
02477	Bayesian machine learning	5	point	Spring F2A (Mon 13-17)
02582	Computational Data Analysis	5	point	Spring F2B (Thurs 8-12)
02586	Statistical Genetics	5	point	Autumn E1A (Mon 8-12)
02807	Computational Tools for Data Science	5	point	E7 (Tues 18-22)
22112	High Performance Computing in Life Science	5	point	Autumn E2A (Mon 13-17)
22115	Computational Molecular Evolution	5	point	Spring F5B (Wed 13-17)
22117	Protein structure and computational biology	5	point	Spring F5A (Wed 8-12)
22145	Immunological Bioinformatics	5	point	Autumn E5A (Wed 8-12)
23257	Compositional data analysis with applications in genomics	5	point	Spring F2A (Mon 13-17)
27641	Systems biology	5	point	Autumn E5B (Wed 13-17)