Biomedical Methods in Life Science

 

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)