BMD3025 : The Application of Bioinformatics Techniques to Biomedical Data (Inactive)
- Inactive for Year: 2025/26
- Module Leader(s): Dr Simon Cockell
- Co-Module Leader: Dr Phillip Aldridge
- Owning School: Biomedical, Nutritional and Sports Scien
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
The aim of this module is to:
1) Deepen students’ understanding of bioinformatics and data science for biosciences
2) Illustrate the practical analysis of biomedical data sets of varying types
3) Provide hands-on experience of designing and implementing an analysis regime
4) Develop practical experience of important computational tools
Outline Of Syllabus
The following topics and themes will be covered in this module:
1) Example scenarios (case studies) of the use of computational analysis in bioscience research
2) The use of distributed version control in analysis teams
3) Analysing high throughput sequencing data
4) Machine learning and artificial intelligence in bioinformatics
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 20 | 1:00 | 20:00 | |
| Scheduled Learning And Teaching Activities | Practical | 5 | 2:00 | 10:00 | PC cluster-based sessions to learn and explore relevant computational skills |
| Guided Independent Study | Project work | 20 | 1:00 | 20:00 | Expected time to spend on group aspects of module assessment |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 4 | 1:00 | 4:00 | Troubleshooting sessions to provide 1-to-1 or 1-to-group technical assistance and guided support. |
| Guided Independent Study | Independent study | 146 | 1:00 | 146:00 | |
| Total | 200:00 |
Teaching Rationale And Relationship
This module builds on the previous experience of stage 3 students, both in the core syllabus but also in the stage 2, semester 2 “Advanced Bioinformatics” module. By this stage, students should be comfortable with using computational approaches to problem solving. This module will lead them towards an understanding of how widely these skills are applied in the biomedical sciences, and how analysis approaches are designed and implemented.
Many of the lectures will take the form of “case studies”, illustrating to students how computational approaches are used to address real problems. Some lectures will also be used to introduce and demonstrate new methodological detail.
Computer-based practicals will allow the students to put these new ideas into practice and will be structured to ensure that the skills required for the assessed elements can be securely built. These sessions will also be focussed on group work, as learning to manage analysis in teams is a key part of the module.
Drop-in sessions will enable students to get focussed technical support where required. These sessions will also enable students to discuss their learning experience with the academic team.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Prob solv exercises | 1 | M | 40 | Managing analysis data and scripts as a team to answer a series of computational problems. Makes use of a distributed version control system, with individual contributions clearly identified. |
| Written exercise | 1 | M | 60 | A flow chart and written analysis plan demonstrating the steps required to solve an example data analysis problem. Max 1500 words. |
Assessment Rationale And Relationship
The assessments together provide a specific framework for evaluating the module learning outcomes.
The group exercise (40%) will demonstrate the student’s ability to work on computational analysis in a team context, and to make use of the technology that has been introduced to them to effectively manage this process. Additionally it will test their ability, developed both in this module and earlier in the programme, to solve problems using computational thinking.
The individual assessment (60%) will help students develop their ability to critically assess available analysis options, and to formulate an analysis plan that is likely to help address specific hypotheses. It will also demonstrate the presentation and communication of complicated ideas.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- BMD3025's Timetable