GBIO0031-1 : Learning from genomic data

Study Programmes 2015-2016

GBIO0031-1

Learning from genomic data

Duration :

150h Proj.

Number of credits :

Master in biomedical engineering (120 ECTS)		5
Master in biomedical engineering (120 ECTS)		5
Master in computer science and engineering (120 ECTS)		5
Master in computer science and engineering (120 ECTS)		5
Master in computer science (120 ECTS)		5
Master in computer science (120 ECTS)		5

Lecturer :

Kristel Van Steen

Language(s) of instruction :

English language

Organisation and examination :

Teaching in the second semester

Units courses prerequisite and corequisite :

Prerequisite or corequisite units are presented within each program

Course contents :

Students are provided with an active research problem that requires carrying out a detailed genome-wide association interaction study (either population-based or family-based). This research problem is provided by the supervisor or via ongoing collaborative efforts.
Depending on the nature of the data students may opt for parametric or non-parametric analysis methods, statistical or machine learning based analytic tools.

Given the interdisciplinary nature of the project work, students can work in groups of 2 or 3. Upon termination of the project, each individual prepares an individual report. There are no class sessions apart from one during which the project problem is introduced. Theoretical and practical guidance is offered upon request.

Learning outcomes of the course :

Given data, students are able to apply and reinforce acquired knowledge on a practical problem in statistical genetics. In particular, students are able to carry out a sound and detailed genetic association interaction analysis, with the most appropriate software tool at hand, covering the following aspect from the analysis pipeline: data cleansing, statistical analysis, interpretation, reporting.

Prerequisite knowledge and skills :

A background in biomedicine and/or informatics is a pro.

Planned learning activities and teaching methods :

During a kick-off meeting, the problem and data are introduced. Students can use their own data upon agreement with the course responsible. Students can work alone (discouraged) or in groups of maximum three students. Several groups may work on the same or different real-life data set (depending upon availability). Supervision is provided via members of the BIO3 group at the EECS department of the School of Applied Sciences. Group meetings with the supervisors may be organized upon request. Upon termination of the project, each individual prepares a report and orally defends it.

Mode of delivery (face-to-face ; distance-learning) :

Primarily distance learning