Successful completion of ‘Clinical Research in Practice (CRiP)’ is required.
Completion of ‘How To Write A Research Proposal’ is recommended.
Completion of the course ‘CRiP - Advanced Concepts in R’ is helpful.
Students enrolled in the master track data@work are particularly encouraged to sign up.
Period: 19 October 2020 - 13 November 2020
Biomedical research increasingly involves the generation and analysis of very large data sets whether it is whole-genome DNA sequencing, gene expression or magnetic resonance imaging data. In particular, large-scale data will be the cornerstone of personalized medicine. This course is aimed at biomedical students who not only want to be responsible for the generation of large-scale data in their future projects, but also want to be able to analyse and interpret their own data.
In this course, students will learn modern methods to identify disease mechanisms and predictive biomarkers of disease risk that are founded on the exploitation of large-scale molecular data sets on human population studies. The focus will be on the analysis of genome-wide genetic, epigenetic and (single cell) gene expression data as well as comprehensive metabolite profiles in blood. The skills acquired in the course can be translated to any research project featuring large data-sets including imaging data in clinical studies or genomics data in experimental animal or cell studies. More generally, the course will help the student to become a future-proof biomedical researcher who is as savvy using a computer as wielding a pipette.
In the course, students will first train in the analysis and interpretation of data generated in human populations (week 1 to 3). Specifically, the student will gain hands-on experience in performing data analysis using actual research data as used in Molecular Epidemiology and will acquire the skills to develop new research proposals in molecular data science from the formulation of hypotheses to designing effective studies. Next, students will apply these newly acquired competences to write a research proposal that follows a data science approach to study ageing as a key example of a complex human trait (week 4).
Research competences: Defining research questions, designing studies in molecular data science, choosing appropriate data analysis techniques, using public data and databases, performing analyses of molecular data, integrating knowledge across biomedical disciplines, and writing, defending and evaluating a research proposal.
Professional competences: Commitment, motivation and drive, reflecting on personal actions, digesting other people’s opinions in work discussions.
Knows how large-scale molecular data can inform on mechanisms and risk of common diseases.
Has insight in modern data analysis methods used to discover molecular signatures of disease phenotypes in genetic, epigenetic, gene expression, and metabolomics data sets.
Get hands-on experience in the analysis and interpretation of genetic, epigenetic, gene expression, and metabolomics data sets.
Shows the ability to develop new researcher project in the field of ageing using molecular data science including background, hypothesis, pilot data, objectives, study design, work plan, and expected outcomes (e.g. causality).
Can perform analyses to generate pilot data in order to critically appraise and, if necessary, reformulate a hypothesis.
Shows communication skills to clearly and convincingly present and defend a research proposal.
Is able to respond constructively to questions/feedback and connecting this feedback to his/her own position regarding his/her own research and in doing so showing an open, self-critical yet firm and self-confident attitude.
Shows professional conduct: being critical yet constructive and eager to improve oneself and in doing so contributes to the learning process of the other students.
Critically and constructively discusses research proposals of peers.
All course and group schedules are published on our LUMC scheduling website or on the LUMC scheduling app.
Mode of instruction
Interactive lectures, computer practicals, self-study assignments, tutor groups.
Handing in assignments. (pass/fail, individually assessed)
Presentation project proposal (background, hypothesis, pilot data, objectives, study design, workplan, expected outcomes). (45%, individually assessed)
Active and critical participation during discussion after project presentations of peers. (15%, individually assessed)
Reflective assignment that shows mastering key aspects of development of research proposal in molecular data science and addressing points raised during peer review. (40%, individually assessed).
In addition, students will during the course (not assessed, but will contribute to successful completion of the course):
o Contribute to interim evaluation of student participation and development during workgroups.
o Fill out project proposal form (preparation of presentation and reflective assignment)
o Participate in peer feedback session in preparation of reflective assignment.
If a student fails to hand in all assignments during the course, the student will be offered limited time after the course to finish them. If done so, the student will pass the course with his or her score based on the weighted average of the presentation, discussion and reflective assignment.
If the weighted average of the presentation, discussion and reflective assignment is below the cut-off of 6, a student will get the opportunity to do a significant compensation assignment. This will have to be finished within limited time after the course. If successful, the student will pass the course with a 6.
Will be distributed during the course.
Registration for FOS courses, H2W, Scientific Conduct, How to start, Course on Animal Science , and CRiP and Adv concepts courses takes place in lottery rounds in the beginning of July. After the lottery rounds: if you want to register for a course you are kindly asked to contact the student administration at email@example.com.