You can find course descriptions in Sisu. In your study plan, choose the course and click the course code or search courses by code or name. Learning environments are found in MyCourses through search or after registration in "My own courses".
Bioinformatics and Digital Health (minor)
Basic information
Code:
SCI3093
Extent:
20–25
ECTS
Curriculum:
2026–2028
2024–2026
2022–2024
Previous curricula
Level:
Advanced studies
Language of learning:
English
Target group:
Students at schools of technology
Teacher in charge:
Harri Lähdesmäki
Administrative contact:
Päivi Koivunen
Organising department:
Department of Computer Science
Organiser:
School of Science
Prerequisites:
No prerequisites for the minor as a whole, some courses may have their own prerequisites.
Quotas and restrictions:
No quotas
Application process:
The minor is open for all master's students at the Aalto University schools of technology.
Content and structure of the minor
About the minor
The Bioinformatics and Digital Health minor in the Life Science Technologies programme is designed to provide students with competence in bioinformatics as well as in biomedical and 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.
After completing the Bioinformatics and Digital Health minor students
- Will have an understanding of computational and probabilistic techniques that are commonly used to analyze biomedical and health data
- Will have the knowledge to apply existing computational methods to real problems
- Will have the necessary background knowledge to develop new computational methods for new applications
- Will be able to communicate the methods and findings to both computational and non-computational experts
Content
| Code | Course name | ECTS | Teaching |
|---|---|---|---|
Select minimum of 15 ECTS |
|||
| MS-C1620 | Statistical Inference | 5 | III |
| CS-E4150 | Digital Health and Human Behavior | 5 | II |
| CS-E5866 | Computational Genomics | 5 | II |
| CS-E5875 | High-throughput Bioinformatics D | 5 | IV |
| CS-E5885 | Modeling Biological Networks D | 5 | III |
| CS-E4885 | Machine Learning in Biomedicine | 5 | I-II |
Select as many courses as needed from the list above and/or below to fulfill the 20–25 ECTS requirement |
|||
| CS-E4715 | Supervised Machine Learning D | 5 | I-II |
| CS-E4825 | Probabilistic Machine Learning D | 5 | III-IV |
| CS-E4890 | Deep Learning | 5 | III-IV |
| CS-E4891 | Deep Generative Models | 5 | IV-V |
Previous curricula
Code: SCI3093
Extent: 20–25 ECTS
Curriculum: 2024–2026
Level: Advanced studies
Language of learning: English
Theme: Health and wellbeing ICT and digitalisation
Target group: Students at schools of technology
Teacher in charge: Harri Lähdesmäki
Administrative contact: Päivi Koivunen
Organising department: Department of Computer Science
School: School of Science
Prerequisites: No prerequisites for the minor as a whole, some courses may have their own prerequisites.
Quotas and restrictions: No quotas
Application process: The minor is open for all master's students at the Aalto University schools of technology.
Content and structure of the minor
About the minor
Description of the minor
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.
Intented learning outco0mes
After completing the Bioinformatics and Digital Health minor students
- Will have an understanding of computational and probabilistic techniques that are commonly used to analyze biomedical and health data
- Will have the knowledge to apply existing computational methods to real problems
- Will have the necessary background knowledge to develop new computational methods for new applications
- Will be able to communicate the methods and findings to both computational and non-computational expertsAbout the minor
You can find course descriptions in Sisu. In your study plan, choose the course and click the course code or search courses by code or name. Learning environments are found in MyCourses through search or after registration in "My own courses".
Content
| Code | Course name | ECTS | Period |
|---|---|---|---|
Select minimum of 15 ECTS |
|||
| MS-C1620 | Statistical Inference | 5 | III-IV |
| CS-C4100 | Digital Health and Human Behavior | 5 | II |
| CS-E5875 | High-throughput Bioinformatics D | 5 | III |
| CS-E5885 | Modeling Biological Networks D | 5 | II |
| CS-E4885 | Machine Learning in Biomedicine | 5 | I-II |
Select as many courses as needed to fulfill the 20–25 ECTS requirement |
|||
| CS-E4715 | Supervised Machine Learning D | 5 | I-II |
| CS-E4825 | Probabilistic Machine Learning D | 5 | III-IV |
| CS-E4890 | Deep Learning | 5 | III-IV |
| CS-E4891 | Deep Generative Models | 5 | IV-V |
Basic information
Code: SCI3093
Extent: 20–25 ECTS
Language of instruction: English
Level: Masters
Theme: Health and wellbeing ICT and digitalisation
Curriculum: 2022–2024
Target group: Students at schools of technology
Teacher in charge: Harri Lähdesmäki
Administrative contact: Päivi Koivunen
Organising department: Department of Computer Science
Prerequisites: No prerequisites for the minor as a whole, some courses may have their own prerequisites.
Quotas and restrictions: No quotas
Application process: The minor is open for all master's students at the Aalto University schools of technology.
Content and structure of the minor
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.
Structure of the minor
Compulsory courses (choose minimum of 20 credits):
| Code | Course name | ECTS credits |
|---|---|---|
| MS-C1620 | Statistical Inference | 5 |
| CS-E5865 | Computational Genomics D | 5 |
| CS-E5875 | High-throughput Bioinformatics D | 5 |
| CS-E5885 | Modelling Biological Networks D | 5 |
| CS-E5890 | Statistical Genetics and Personalised Medicine D* | 5 |
| CS-E4880 | Machine Learning in Bioinformatics D* | 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 5 credits if needed):
Basic Information
Code: SCI3093
Extent: 20 - 25 credits
Language: English
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.
Content and structure of the minor
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.
Structure of the minor
Compulsory courses (choose minimum of 20 credits):
| Code | Course name | ECTS credits |
|---|---|---|
| Compulsory courses (choose minimum of 20 credits): | ||
| MS-C1620 | Statistical Inference | 5 |
| CS-E5865 | Computational Genomics | 5 |
| CS-E5875 | High-throughput Bioinformatics | 5 |
| 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):
Basic Information
Code: SCI3064
Extent: 20 - 25 credits
Language: English
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.
Content and structure of the minor
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.
Structure of the minor
Compulsory courses (choose minimum of 20 credits):
| Code | Course name | ECTS credits |
|---|---|---|
| MS-E2115 | Experimental and Statistical Methods in Biological Sciences | 5 |
| CS-E5865 | Computational Genomics | 5 |
| CS-E5875 | High-throughput Bioinformatics | 5 |
| 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):
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
Compulsory courses 10 cr
| Code | Course name | ECTS credits |
|---|---|---|
| CS-E5860 | Computational Genomics | 5 |
| CS-E5870 | High-throughput Bioinformatics | 5 |
Elective courses 10-15 cr
Select as many courses as needed to fulfill the 25-credit requirement
| Code | Course name | ECTS credits |
|---|---|---|
| MS-E2115 | Experimental and Statistical Methods in Biological Sciences | 5 |
| CS-E5880 | Modelling Biological Networks | 5 |
| CS-E5890 | Statistical Genetics and Personalised Medicine | 5 |
| CS-E4860 | Special Course in Bioinformatics II | 5 |
| CS-E3210 | Machine Learning: Basic Principles | 5 |
| CS-E4830 | Kernel Methods in Machine Learning | 5 |
| CS-E4820 | Machine Learning: Advanced Probabilistic Methods | 5 |
| CS-E4840 | Information Visualization | 5 |