CSC3432 : Biomedical Data Analytics and AI
CSC3432 : Biomedical Data Analytics and AI
- Offered for Year: 2025/26
- Module Leader(s): Professor Jaume Bacardit
- Co-Module Leader: Dr Pawel Widera
- Owning School: Computing
- 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 | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
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
Learning Outcomes
Intended Knowledge Outcomes
1. To assess the analytical challenges underlying the data being generated in biological/biomedical
environments
2. To apply the main classes of algorithms (eg optimisation, machine learning) that can be used to analyse such
data
3. To implement the principles of algorithm design for a range of biological/biomedical data types
4. To apply the protocols required to validate and extract value from the outputs of the biological/biomedical
data analytics process
5. To assess the rationale for, advantages and limitations of existing biological/biomedical data analytics
algorithms
Intended Skill Outcomes
1. To apply knowledge of computational, mathematical and statistical techniques for the storage and analysis of
biological/biomedical data.
2. To select the most appropriate (combination of) algorithms for a given biological/biomedical data analysis
3. To present the results of a given analysis in such a way that it can provide real value/insight to
biologists/clinical researchers
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Structured Guided Learning | Lecture materials | 30 | 1:00 | 30:00 | Asynchronous online materials. |
| Guided Independent Study | Assessment preparation and completion | 10 | 1:00 | 10:00 | Formative assignment preparation. |
| Guided Independent Study | Assessment preparation and completion | 70 | 1:00 | 70:00 | Coursework preparation. |
| Scheduled Learning And Teaching Activities | Practical | 12 | 2:00 | 24:00 | Practicals (in person). |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 11 | 1:00 | 11:00 | Tutorial (in person). |
| Guided Independent Study | Independent study | 30 | 1:00 | 30:00 | Lecture follow-up. |
| Guided Independent Study | Independent study | 25 | 1:00 | 25:00 | 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.
Reading Lists
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 | 100 | Report on biomedical data analysis (maximum 3000 words). |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Written exercise | 1 | M | Short piece of writing on the design of biomedical machine learning experiments (maximum 500 words). |
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.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC3432's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- CSC3432's past Exam Papers
General Notes
N/A
Welcome to Newcastle University Module Catalogue
This is where you will be able to find all key information about modules on your programme of study. It will help you make an informed decision on the options available to you within your programme.
You may have some queries about the modules available to you. Your school office will be able to signpost you to someone who will support you with any queries.
Disclaimer
The information contained within the Module Catalogue relates to the 2025 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, staffing changes, and student feedback. Module information for the 2026/27 entry will be published here in early-April 2026. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.