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.

Goals [Dublin descriptors]
• At the end of the course, students will be aware of Bioinformatics
methods that are applicable to the area of Translational Medicine
[knowledge and understanding].
• Students should be able to combine these methods to come to a
creative solution to get new insights from large-scale biological
experiments [applying knowledge and understanding].
• At the end of the course, students 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 [applying knowledge and understanding].
* At the end of the course, students will be able to work as a team to
execute a large, complex biomedical bioinformatics project, and report
on the results [communication]
• The student will become 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 [making
judgements, learning skills].

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%)

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".