Duration
150h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course strengthens Scientific Problem Solving by engaging students in authentic research on large-scale genomic data. Through project-based research, it develops Practical & Professional Competence, reinforces Interdisciplinary Teamwork, and promotes Ethical & Sustainable Design through responsible interpretation and reporting of complex biomedical data.
Students are given an active research problem that requires a detailed large-scale genomic analysis. The specific research problem is provided by the supervisor or through ongoing collaborative work. This year the course focuses on machine-learning interaction proxies, biological interaction networks, and the use of these tools to detect individual heterogeneity and study epistasis.
The course is organized around a structured analysis pipeline that guides the project from raw data to interpretation. In particular, students are expected to work through the following steps: gene-level representation through SNP-to-gene aggregation; selection of a prediction tool together with pairwise interaction scores; use of those scores as edge weights in a gene interaction network; construction of individual-specific molecular networks; and a final discussion of the biological and statistical meaning of the resulting interaction signals.
Depending on the nature of the data, students may choose parametric or non-parametric analysis methods, as well as statistical, machine-learning, or network-based tools. Any technique from previous courses may be used, provided that it is appropriate for the problem at hand.
Given the interdisciplinary nature of the project, students can work in groups of 2 or 3. At the end of the project, each student prepares an individual report. There are no class sessions apart from the introductory meeting in which the project problem is presented. Theoretical and practical guidance is offered upon request.
Learning outcomes of the learning unit
This course is part of an integrated learning pathway in Computational and Systems Genomics. The pathway follows a deliberate progression: students first understand the data (GBIO0002), then understand the scientific questions and evidence (GBIO0015), next develop and evaluate computational methods (GBIO0030), and finally apply these competencies in an authentic research project (GBIO0031). As the capstone of the learning pathway, this course enables students to integrate and apply the knowledge and skills acquired throughout the curriculum in an authentic research project, reinforcing practical and professional competence through independent scientific investigation.
Using the provided data, students are able to apply and reinforce knowledge acquired in a practical problem in statistical genetics. In particular, students are able to carry out a sound and detailed omics data analysis, using the most appropriate software tool at hand, covering the following aspects of the analysis pipeline:
- data cleansing;
- statistical analysis;
- interpretation;
Prerequisite knowledge and skills
A background in biostatistics, bioinformatics, or statistical genetics is a plus. Alternatively, one of the following courses have been/ are taken: GBIO0002, GBIO0015 or GBIO0030.
Planned learning activities and teaching methods
During a kick-off meeting, the problem and data are introduced. Students may use their own data with the agreement of the course responsible. Students can work alone, although this is discouraged, or in groups of up to three students. Several groups may work on the same or on different real-life data sets, depending on availability.
Supervision is provided by members of the BIO3 group at the GIGA or the EEI department of the Faculty of Applied Sciences. Group meetings with the supervisors may be organized upon request. At the end of the project, each student prepares a report and defends it orally.
The teaching approach is predominantly project-based learning, with research-based learning as a secondary component. The course relies on student autonomy, with supervisory guidance available on request or when deemed essential.
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Further information:
Primarily distance learning
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Further information:
There is no mandatory textbook. Essential information is posted on the course website (eCampus) unless specified otherwise.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions ) AND oral exam
Further information:
Final grading is based on an individual project report (60%) and its individual oral defence (40%). The report assesses the formulation of the research problem, appropriateness and justification of the analytical strategy, technical validity of the analysis, interpretation of the results, and critical and creative scientific reflection. The oral examination assesses the student's individual understanding of the work, ability to justify methodological choices, critically evaluate results and limitations, and reason about alternative analytical strategies.
A minimum mark of 10/20 for the report is required for admission to the oral examination.
For the second examination session, students are not required to repeat or extend the original data analysis. If the report obtained at least 10/20 but the student did not pass the course, the report mark is retained and only the oral examination is retaken. If the report obtained less than 10/20, the student may submit a revised version addressing deficiencies in methodological justification, interpretation, critical reflection and presentation, without carrying out a new data analysis. Admission to the second-session oral examination again requires a report mark of at least 10/20.
The oral examination in the second session is based on the original project and assesses, in particular, whether the student can identify weaknesses in the original approach, explain how these could be addressed, and critically discuss appropriate alternative analytical strategies.
Work placement(s)
Organisational remarks and main changes to the course
The project work is organized in the second semester. Exam in June. Depending on the number of students who enrol on this course, the content and practical organization of the project work may be adapted (discussed in class).
Contacts
Kristel Van Steen - e-mail kristel.vansteen@uliege.be
Assistant: to be communicated
Preferred contact mode: e-mail (mention GBIO0031 in the subject title) or personal contact, after a lecture or by appointment.