Bioinformatics for Translational Medicine

Course Objective

Observations from biological high-throughput experiments allow us to improve diagnosis and give a personalized treatment plan for patients. However, integrating data from several sources and using this data for predictions is non-trivial.

This is a theoretical and practical Bioinformatics course on computational methods for Translational Medicine; we will focus on Bioinformatics methods that are used to predict the clinical outcome for patients and analysis methods to obtain deeper understanding of complex diseases, by combining data from various high-throughput experiments such as proteomics, microarrays and next-generation sequencing as well as existing biological databases.

After completing the course successfully,

• You can interpret research results in the context of biology
• You will be aware of Bioinformatics methods that are applicable to the area of Translational Medicine
• You can combine these methods into a creative solution using large-scale biological experiments
• You will have hands-on experience in handling large biological datasets, and will understand the complexity of the biological data both from high-throughput experiments and existing biological databases
• You will be able to work as a team to execute a large, complex biomedical bioinformatics project, and report on the results
• You will be familiar with a few in-depth research topics that lie within the expertise area of several (Bioinformatics) researchers, among others from the VU, UvA, AUMC and NKI

Course Content

Theory

• Computational analysis of molecular profiling techniques, such as WGS, WES, proteomics, RNA sequencing, and arrayCGH.
• Computational methods applied to these data types, such as: machine learning, normalisation, regularization, feature selection, classification, read mapping, clustering.
• All data analysis is relevant in a clinical setting, for diagnosis, treatment decisions or biomarker discovery.

Practical

• A large assignment for which you have to build a classifier based on molecular profiling data (the precise data-set is to be determined). You need to hand in predictions, write a paper and give a presentation. Note that this is a group project, and to participate in this practical you need to have obtained at least 12 ECT from Master-level courses.
• A smaller assignment that involves analysing and understanding molecular profiling data of another modality than the large classifier assignment.

Additional Information Teaching Methods

• 13 lectures (2 two-hour lectures per week)
• 12 computer practicals (2 two-hour sessions per week)

Method of Assessment

The final grade for this course will consist of 50% practical work (see above) and 50% theoretical assessment.

Practical assessment (50%):

• Classifier assignment (45%)
• Other data modality assignment (5%)

(assessment of practical assignments may be adjusted based on ongoing developments in generative language models)

Theoretical assessment (50%):

• Oral or written exam (depending on number of course students)
• The exam is based on a selection of 8-10 scientific papers in the field of Bioinformatics & Translational Medicine.

Entry Requirements

Importantly: to participate in the group assignment, you need to have obtained a minimum of 12 ECT of Master level courses. In addition, some basic programming skills (either in R or Python) are required, as well as some basic knowledge on molecular biology. If you are not following the Master Bioinformatics and Systems Biology, it is advisable to first follow the Master course "Fundamentals of Bioinformatics".

Literature

• Course material on Canvas, including slides and recordings of lectures.
  
• 8-10 scientific papers are provided, and make up the course syllabus.

Additional Information Target Audience

M Artificial Intelligence
M Bioinformatics and Systems Biology
M Biomolecular Sciences
M Oncology
M Computational Science

Additional Information

The course is taught in English.

• Compulsory course for students in Master Bioinformatics and Systems Biology
  
• Optional course for students with a Bachelor in Physics, Chemistry, Mathematics, Computer Science, Biology, or Biomedical Sciences (see requirements below).

Recommended background knowledge

If you are not following the Master Bioinformatics and Systems Biology, it is advisable to first follow the Master course "Fundamentals of Bioinformatics".