Biomedical Methods in Life Science - Study line
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
Programme specific courses (55 ECTS)
A total of 55 ETCS must be completed in Program specific courses. Program specific clourses consists of 10 ECTS mandatory courses that teaches essential skills relating to the specific field of this programme, and 5 ECTs from courses relate to innovation, entrepreneurship & management. The remaining 40 ECTs are selected from the course list below
Program specific 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 |
Other Program specific 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) |
| 22140 | Introduction to Systems Biology | 5 | point | Autumn E1B (Thurs 13-17) |
| 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) |