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Bioinformatics

Code: SCI3064

Extent: 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

15

T-61.5120

Computational Genomics

5

T-61.5050

High-throughput Bioinformatics

5

Elective courses

10

Select as many courses as needed to fulfill the 25-credit requirement

Becs-114. 5501

Experimental and Statistical Methods in Biological Sciences I

5

T-61.5110

Modelling Biological Networks

5

T-61.6070

Special Course in Bioinformatics I

5

T-61.6080

Special Course in Bioinformatics II

 5

T-61.3050

Machine Learning: Basic Principles

 5

T-61.3025

 Principles of Pattern Recognition

 5

ICS-E4030

Kernel Methods in Machine Learning

 5

T-61.5140

Machine Learning: Advanced Probabilistic Methods

 5

T-61.5010

Information Visualization

 5

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