Undergraduate Course: Programming for Biomedical Informatics (INFR11260)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The scale and diversity of biomedical data being generated globally is facilitating a step-change in our understanding of human disease. To take advantage of these data effectively we need to use different computational techniques to retrieve, parse, and analyse them.
This course will consist of a series of lectures and tutorials covering a selection of advanced programming topics in Python specifically tailored to the analysis of biomedical data.
The course aims to equip students will the skills and experience needed to pursue future specialist courses in biomedical informatics and related disciplines. |
| Course description |
In this course, students will learn how to use Python to retrieve and parse data from biological repositories through bulk download and application programming interfaces (APIs). They will learn about established data formats for different data modalities so that they understand the structure and content of the data they are using and how it was generated. Each week we will focus on analytical tasks in linked topics that span the main components of modern biomedical informatics research. Topics will change slightly each year, but will typically include tools, algorithms, and approaches for biological sequence, multi-omics (transcriptomics, proteomics, methylomics), biomedical network, and biomedical text analysis. Each topic will be explored using real-world examples.
The course will be taught in twice weekly paired lectures, with the first lecture introducing background material, programming concepts, and techniques and the second lecture demonstrating their applied use in an interactive tutorial style. The course will be supported by Jupyter Notebooks for each topic that will contain technical programming examples and exemplars of their application to real-world biomedical data.
Summative assessment will consist of weekly problems in each topic area and a final exam based on the lecture and tutorial content.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | Students must be proficient in basic Python coding. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Summative Assessment Hours 2,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Exam 80%
Coursework 20%
Summative coursework assessment will take the form of a weekly programming challenge either as a multiple-choice questionnaire or a small coding problem based on the corresponding week's material. |
| Feedback |
Feedback will be provided in interactive lecture/tutorials in the second teaching session each week. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Select sources of biomedical data appropriate for a given research question.
- Determine the most suitable methods to use to analyse these data.
- Implement and critically evaluate advanced Python code for biomedical data projects using reproducible research practices.
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Reading List
| General references and specific weekly references will be in the Resource List for the course. |
Additional Information
| Graduate Attributes and Skills |
- Problem-solving
- Critical/analytical thinking
- Knowledge integration
- Cross-disciplinary communication |
| Keywords | Biomedical-informatics,Bioinformatics,Python,Programming,PBI |
Contacts
| Course organiser | Dr Ian Simpson
Tel: (0131 6)50 2747
Email: Ian.Simpson@ed.ac.uk |
Course secretary | |
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