All courses funded by this research school will make all of its material freely available online to everyone in the world using the Creative commons 3.0 license. Lectures may be recorded on video and put online.
By Board decision, we will provide a completion certificate to students that pass FOUR of the required courses and attend two Annual Meetings. The certificate outlines the school’s goals and obligatory elements, and describes the competitive acceptance criteria. This certificate is not a degree and does not itself confer any additional ECTS points.
Compulsory courses (3-5 hp)
The basis for this research school is two compulsory courses per year for the students. The courses are progressive, i.e. the learning outcomes from the first course are a necessary requirement for the next course. The earlier courses are aimed at teaching important computational methods, while the later courses are aimed at relevant high-throughput methods for medical bioinformatics.
1. Applied Bioinformatics, 5 hp (Lars Arvestad, SU). In this course the students will be taught a good working habits for bioinformatics. They will learn to work in Unix, using Python for scientific code development and learn about good practices for scientific programming. Tentative maximum enrollment of 30 students.
- Runs 10-14 June 2019.
2. Algorithms in Bioinformatics, 5 hp (Lukas Käll, KTH). This course will provide an in depth understanding some of the most important algorithms used in bioinformatics. The students will learn how to apply fundamental algorithm design methodologies such as dynamic programming, clustering to problems in computational biology and bioinformatics.
- Runs given 30 September to 4 October 2019.
3. Machine Learning in Medical Bioinformatics, 5HP (Björn Wallner, LiU). In this course the students will learn statistical and machine learning methods that are widely used in medical bioinformatics today. Last ran from 13:00 on 20 May to 12:00 on 24 May, 2019. Next edition is from 13:00 on 11 May, to 12:00 on 15 May, 2020. Note that this is very soon after the likely dates of the Annual Meeting. Older course materials are published — Click here for publicly available course materials.
4. Experimental design for high-throughput technologies, 3 HP (Carsten Daub, KI). In this important course the students will learn how to design the bioinformatics analysis of high throughput experiments. Given the background from the three earlier methods they should be able to design a pipeline that can easily be extended for more advanced analysis.
- Runs from 12:00 on September 2 to 16:00 on September 6, 2019, in Solna. The room is posted on the Canvas site, to which second-year students will be invited in due course.
5. Variation interpretation and structural bioinformatics, 5HP (Mauno Vihinen, LU). In this course the student will learn how to use information from personal genomics studies to interpret the effects of small and large variations. The students will also learn how to use structural bioinformatics tools predictions and analyses of variant effects and mechanisms.
- Click here for publicly available course materials from the 2019 edition.
- To run 20-24 January 2020. Third year-students to receive an invitation.
6. Computational Systems Biology (Mika Gustafsson, LiU). Students will study basic systems biology and its application in biomedical research and medical problems. The course introduces basic concepts and mathematics underlying modern systems biology. You will understand the differences between small-scale and large-scale models using examples from biomedical research. The course will emphasise large-scale systems biology methods.
- Click here for publicly available course materials.
- We ran for the first time from 10:15 AM on 20 May to 15:00 on 24 May, 2019.
- Next session will run from 10:15 AM on 11 May to 15:00 on 15 May, 2020.
Rebecka Jörnsten will provide the course “Modern large-scale factorization and dimension reduction methods” for the first time, in the Spring of 2020. This is an advanced course which is outside of the established sequence, and which we think will appeal to many (though not necessarily all) MedBioInfo students.
The second compulsory part of the research school is the Annual Meeting. In addition, to increasing the communication between the medical and informatics community the Annual Meeting is also an important part of the education of the Ph.D. students. The Annual Meeting will include
1. Training in presentation and writing skills.
2. Thematic group meetings and presentations.
3. Individual mentoring by their bioinformatics mentor.
4. Social activities
5. Research talks by students and PIs.
6. Unstructured time for discussions.
The next Annual Meeting is scheduled for 18-20 May, most likely at Varbergs Kusthotell near Göteborg.
You can earn a MedBioInfo completion certificate, by completing five MedBioInfo courses, and attending two Annual Meetings. This is not the same as a degree, as degrees are only issued by universities. The Studierektor can, upon student request, review a sufficiently-similar non-MedBioInfo course and determine whether it can be counted toward the above course requirement.
There are of course many fine courses available outside the Research School, in Sweden, abroad, or online, which we encourage students to take as appropriate. Programming courses are important for first-year students who have not had them (or the equivalent experience). SciLifeLab offers courses — the Python may be particularly appropriate. Check the registration deadline for this programming course, in 2017 it was in September.. Some of the core courses require programming, and students are responsible for meeting all prerequisites in time to take courses.
One of the aspects of this program is to improve the supervision of the students. Here, we propose to use a modified version of the idea used in Gothenburg some time ago with two supervisors for each student, one bioinformatician and one medical scientist. Our goal is that this program each student will be assigned a mentor from the Swedish bioinformatics community. The mentor should be from another research constellation than the student. The role of the mentor will be to help the student identify and solve computational bottlenecks in their research project, and also to provide more general career advice. The student will discuss with the mentor at least twice per year, either via electronic meetings or by short visits. We suggest using this Student-Mentor-meeting-form to guide student-mentor discussions.