Admission requirements
Completion of both of both ' Clinical Research in Practice’ and ‘How To Write A Research Proposal’ is recommended.
Students enrolled in the master track ‘Medical Genomics and Data-driven research’ are particularly encouraged to sign up.
Description
Period: 18 November 2024 - 13 December 2024
Biomedical research increasingly involves the generation and analysis of very large data sets. Such data sets include whole-genome DNA sequencing, gene expression or magnetic resonance imaging data, and 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, you will learn modern methods to identify mechanisms and biomarkers of disease that are founded on the exploitation of large-scale molecular data sets in 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 you to become a future-proof biomedical researcher who is as savvy using a computer as wielding a pipette.
In the course, you will first train in the analysis and interpretation of data generated in human populations (week 1 to 3). Specifically, you will gain hands-on experience in performing data analysis using actual research data and will acquire the skills to develop new research proposals in molecular data science from the formulation of hypotheses to designing effective studies. Throughout, you will use the software R as a tool to perform the analyses; the focus is not on learning how to code. The students will apply these newly acquired competences to develop a research proposal in the field of molecular data science focusing on ageing-related disease outcomes (week 3 and 4). This part of the course includes the analysis of pilot data to support the hypothesis, feed-back sessions with tutors during the developmnt of the proposal, and a real-life experience of how background knowledge combined with creativity and discussions can result in novel science over a relatively short period.
The complete course will be in person between 9.15 and 17.00 on weekdays. No additional work is expected beyond these hours.
Course objectives
The student:
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.
Timetable
All course and group schedules are published on MyTimeTable.
The exam dates have been determined by the Education Board and are published in MyTimeTable.
It will be announced in MyTimeTable and/or Brightspace when and how the post-exam feedback will be organized.
Mode of instruction
Interactive lectures, computer practicals, self-study assignments, tutor groups.
Assessment method
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.
Reading list
Will be distributed during the course.
Registration
Registration for FOS courses, H2W, Scientific Conduct, Course on Lab Animal Sciences and CRiP 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 masterbms-courses@lumc.nl.