Undergraduate Course: Biomedical Informatics 3 (IBMS09008)
Course Outline
| School | Deanery of Biomedical Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 9 (Year 3 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Biomedical Informatics 3 (BMI3) builds on Year 2 BMI courses and aims to introduce students to key concepts in the use and application of algorithms to biomedical problems, and to introduce key aspects of image analysis, the principles of microscopy used to generate digital images and the application of current algorithms and software for image analysis. In addition, this course will have a focus on communication and joint project development between bioinformaticians and biomedical scientists. |
| Course description |
Biomedical Informatics 3 provides students the opportunity to develop their skills and understanding of two key themes: search/optimisation algorithms and image analysis. The course will provide a solid theoretical foundation on various algorithms, with an emphasis how these algorithms can be applied to biomedical problems. The second theme of the course is image analysis, as biomedical research and clinical image data is increasingly requiring quantitative processing and analysis skills. The course will both provide basics of microscopy and introduce commonly-used software with the aim of highlighting key steps of image processing and analysis where information loss or distortion may occur. As image analysis commonly requires application of various algorithms, the practicals of this course specifically aim to bridge the gap between the theoretical knowledge and application of algorithms. To facilitate the student's skills for the application of algorithms for biomedical data analysis and to improve their problem-solving skills, the course also introduces sessions for advancing communication with biomedical scientists and software engineers.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2021/22, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 28,
Seminar/Tutorial Hours 14,
Supervised Practical/Workshop/Studio Hours 56,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
98 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
50 %,
Practical Exam
50 %
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| Additional Information (Assessment) |
A computer-based timed exam, solving simple real-life problems, including writing simple algorithms and using image analysis tools (individual, 50%).«br /»
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A 'mini-project' involving choosing and solving a more difficult problem (group-based, assessed based on a written report, 30%, and oral presentation, 20%). |
| Feedback |
Students will receive summative feedback on their exam and group project. Additional formative feedback will be given throughout the course, especially in practical sessions, so students can check their understanding as they progress, as well as to the mid-term exam. This includes opportunities for formative peer feedback. Students are also encouraged to add a reflective element to their practical write-ups. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Describe and discuss the main principles of algorithms for search and optimization, and how they can be used to solve biomedical problems.
- Describe and discuss the main principles of microscopy and apply algorithms and software for image processing and analysis.
- Be able to apply their algorithm and image processing knowledge to solve a real-life problem in a group setting.
- Communicate effectively with developers & biomedical scientists, by understanding and working to specifications, and designing and developing pipelines with biomedical scientists.
- Demonstrate specialist knowledge on data protection and data ethics.
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Additional Information
| Graduate Attributes and Skills |
This course follows the core areas of technological knowledge for Biomedical Informatics, as identified by the American Medical Informatics Association (Kulikowski et al., J Am Med Inform Assoc 19, 2012): Information documentation, storage, and retrieval. In addition, the course develops the following core graduate skills and attributes for graduates in Bioinformatics, as identified by Welch et al. (2014) PLoS Comp Biol 10(3).
Computing:
Algorithm design and analysis, ability to use scientific analysis software packages, open source software repositories.
Bioinformatics:
Analysis of biological data; ability to manage, interpret, and analyze large data sets; broad knowledge of bioinformatics analysis methodologies; expertise in common bioinformatics software packages, tools, and algorithms.
Statistics and Mathematics:
Mastery of relevant statistical methods (including experimental design, descriptive and inferential statistics, analysis of next generation sequencing data using R).
Biology:
Cell biology.
General:
Time management, project management, independence, ability to synthesize information, ability to complete projects, critical thinking, ability to communicate scientific concepts, analytical reasoning, scientific creativity, collaborative ability. |
| Keywords | algorithms,image analysis,ethics,professionalism |
Contacts
| Course organiser | Dr John Menzies
Tel: (0131 6)51 1711
Email: John.Menzies@ed.ac.uk |
Course secretary | Miss Natasha Goldie
Tel:
Email: natasha.goldie@ed.ac.uk |
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