Code: SCI3064
Extent: 20 - 25 credits
Language: English
Teacher in charge: Harri Lähdesmäki
Target group: Students interested in developing and applying computational methods in biological, biomedical and bioeconomy applications. In particular, the minor is designed to complement any major in the Life Science Technologies programme, as well as the major Machine Learning and Data Mining.
Application procedure: The minor is open for all master's students at the Aalto University schools of tehcnology.
Quotas and restrictions: No quotas
Prerequisites: No prerequisites for the minor as a whole, some courses may have their own prerequisites.
Content and structure of the minor
The Bioinformatics minor in the Life Science Technologies programme is designed to provide students with competence in bioinformatics and computational systems biology. The minor equips students with skills and tools to develop new computational methods and models and to apply them to real world biomolecular data. Computer practicals are part of most courses ensuring understanding of both theory and practice of the methods. The biological background knowledge can be broadened with an elective minor.
State-of-the-art methods for analysing next-generation sequencing, microarray and other omics data as well as biological networks are part of the curriculum. Examples of research questions studied include predicting drug-target interactions, reconstructing biological networks, finding associations between genotypes and diseases, and modelling dynamical behaviour of complex biological pathways.
Structure of the minor
Code | Name | Credits |
Compulsory courses | 10 | |
Computational Genomics | 5 | |
High-throughput Bioinformatics | 5 | |
Elective courses | 10 - 15 | |
Select as many courses as needed to fulfill the 25-credit requirement | ||
Experimental and Statistical Methods in Biological Sciences | 5 | |
Modelling Biological Networks | 5 | |
Statistical Genetics and Personalised Medicine | 5 | |
Special Course in Bioinformatics II | 5 | |
Machine Learning: Basic Principles | 5 | |
Kernel Methods in Machine Learning | 5 | |
Machine Learning: Advanced Probabilistic Methods | 5 | |
Information Visualization | 5 | |