CSC3432 : Biomedical Data Analytics and AI
- Offered for Year: 2023/24
- Module Leader(s): Dr Jaume Bacardit
- Co-Module Leader: Dr Pawel Widera
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
Aims
1. To familiarise students with the fundamental computational approaches used for tackling biological and
biomedical data handling and analysis
2. To introduce the concepts of algorithm design for biological/biomedical data
3. To develop skills in algorithm design with an emphasis on solving biological/biomedical problems
4. To understand the most appropriate type of algorithms for differing analytical problems in biology and
biomedicine and to introduce some of the most appropriate implementation strategies.
Outline Of Syllabus
1. The broad spectrum of data types in biology and biomedicine
2. Basic concepts of cell and molecular biology
3. Algorithms for biological sequence comparison
4. Algorithms for structural bioinformatics
5. Artificial Intelligence approaches for the analysis of biological and biomedical data
6. Biological/Biomedical data preprocessing
7. Biological significance of biomedical data analysis
8. Analysis of biological/biomedical sequence data
9. Analysis of biological/biomedical imaging data
10. Analysis of biological/biomedical tabular data
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 30 | 1:00 | 30:00 | Lecture follow-up |
| Structured Guided Learning | Lecture materials | 30 | 1:00 | 30:00 | Lectures non-synchronous online |
| Scheduled Learning And Teaching Activities | Practical | 11 | 2:00 | 22:00 | Practicals, synchronous PIP sessions. |
| Guided Independent Study | Project work | 40 | 1:00 | 40:00 | Coursework 2 |
| Guided Independent Study | Project work | 40 | 1:00 | 40:00 | Coursework 1 |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 11 | 1:00 | 11:00 | Synchronous PIP sessions, if avail. Otherwise additional synchronous online session. Ask Qs re lecs |
| Guided Independent Study | Independent study | 27 | 1:00 | 27:00 | Background reading |
| Total | 200:00 |
Teaching Rationale And Relationship
Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.
This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes. Students are expected to spend time on coursework outside timetabled practical classes.
Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Practical/lab report | 1 | M | 50 | max 2000 words on biological data analysis |
| Practical/lab report | 1 | M | 50 | max 2000 words on biomedical data analysis |
Formative Assessments
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Practical/lab report | 1 | A | Draft report, max 500 words, on the design of a biomedical machine learning experiment |
Assessment Rationale And Relationship
This module focuses on a very practical subject and hence an assessment based on coursework is the best option to evaluate the student’s knowledge. The coursework will assess the student’s ability to apply the module’s concepts in the a practical setting and will be assessed as practical reports, which is a suitable method for assessing the use of biological data analytics software.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC3432's Timetable