Extent: 20 - 25 credits
Teacher in charge: Harri Lähdesmäki
Administrative contact: Study coordinator Päivi Koivunen
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.
The Bioinformatics and Digital Health minor in the Life Science Technologies programme is designed to provide students with competence in bioinformatics and biomedical/health data analysis methods. 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.
State-of-the-art methods for analysing next-generation sequencing 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, identifying disease biomarkers from biomedical and health data, and modelling dynamical behaviour of complex biological pathways.
Compulsory courses (choose minimum of 20 credits):
Experimental and Statistical Methods in Biological Sciences
|CS-E5885||Modelling Biological Networks||5|
|CS-E5890||Statistical Genetics and Personalised Medicine*||5|
|CS-E4880||Machine Learning in Bioinformatics*||5|
|*CS-E5890 and CS-E4880 are lectured every other year (alternating). CS-E5890 is lectured in odd years and CS-E4880 is lectured in even years.|
Elective courses (select as many courses as needed to fulfill the 20-25 credit requirement):
Machine Learning: Basic Principles
Kernel Methods in Machine Learning
Machine Learning: Advanced Probabilistic Methods