June 18 - July 6, 2018.
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.
This course foucses on Molecular Data Science: from disease mechanisms to personalized medicine.
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 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 and 2). 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 3).
This course will particularly work on:
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.
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.
Mode of instruction
Interactive lectures, computer practicals, self-study assignments, tutor groups.
Student behavior during workgroups (motivation, independency, oral reporting, participation in discussion, handing in assignments). (10%, individually assessed)
Critical and constructive participation during the week devoted to developing the project proposal (formulating hypotheses and study design). (10%, individually assessed)
Presentation project proposal (background, hypothesis, pilot data, objectives, study design, workplan, expected outcomes). (35%, assessed in duos)
Active and critical participation during discussion after project presentations of peers. (10%, 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. (35%, individually assessed)