BMD3021 : Omics and AI for emerging and future biomedicine (Inactive)
- Inactive for Year: 2025/26
- Module Leader(s): Dr Daniel Williamson
- Co-Module Leader: Dr Amir Enshaei
- Lecturer: Dr Sarra Ryan
- 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:
Introduce students to how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising Bioscience research and the important role of omics data in precision medicine.
To equip students with knowledge of how AI/ML can be applied within omics disciplines such as genomics, transcriptomics, and proteomics and the huge potential for future advances in biomedical healthcare.
Outline Of Syllabus
Topics covered by this module include:
Foundations: Introduction to advanced key concepts and technologies in omics and AI/ML for precision medicine
Applications: For example, AI/ML in pharmacogenomics, drug discovery/repositioning, risk prediction, diagnosis and clinical trials
Methodologies: For example, deep learning, natural language processing, generative models, multiomic data integration.
Ethics and Challenges: Addressing biases, privacy, regulation, and misconceptions of AI in biomedicine
Real-World Impact: Practitioner-led applications in specific fields, for example, oncology, liver disease, dementia, microbiome.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 18 | 1:00 | 18:00 | Mixture of didactic and flip classroom sessions |
| Structured Guided Learning | Academic skills activities | 3 | 1:00 | 3:00 | Various skills activities including technical exercises, critical thinking exercises and virtual lab classes. |
| Structured Guided Learning | Structured research and reading activities | 3 | 1:00 | 3:00 | Undertaking research as individuals/groups for a presentation and/or student-led contribution to small group teaching |
| Scheduled Learning And Teaching Activities | Small group teaching | 3 | 1:00 | 3:00 | Discussion based seminar sessions with some student-led contributions/presentation. |
| Scheduled Learning And Teaching Activities | Workshops | 1 | 2:00 | 2:00 | Interactive session and group work as formative assessment as revision and preparation for summative assessment |
| Guided Independent Study | Independent study | 171 | 1:00 | 171:00 | Includes: Assessment preparation and completion, directed research and reading, skills practice, reflective learning activity and student-led group activity. |
| Total | 200:00 |
Teaching Rationale And Relationship
Lectures i.e. formal taught sessions to the whole cohort consisting largely of the exposition of theory, themes, methodologies and techniques. These will support a student’s foundational understanding of AI/ML and omics in precision medicine (Knowledge Outcome 1 & Skill Outcome 1), illustrate applications and analyses (Knowledge Outcome 2,3) and highlight critical perspectives introducing key debates and controversies, enabling students to critique and reflect on challenges in the field (Knowledge Outcome 3,4).
Seminars / Small group teaching will encourage contextualisation of taught material through interactive discussions allowing students to explore concepts in detail, clarifying any doubts and reinforcing understanding also provides a space to articulate their knowledge and learn from peers to internalise key ideas. Q&As, data interpretation and problem-solving all will encourage critical thinking and analysis (Skill Outcomes 2,4,5), developing analytical skills (Skill Outcomes 1,3). Small group teaching provides an opportunity to support for students in their preparation for their assessment.
Guided learning including self-paced study with structured resources, tutorials, or targeted exercise will support each skill outcome fostering independent, personalised, and reflective learning experiences. Guided exercises like problem-solving scenarios or guided research tasks help students analyse AI techniques and their applications in real-world settings. Targeted resources help students build foundational knowledge at their own pace (Knowledge Outcome 1 & Skill Outcome 1). Structured tasks, such as completing worksheets or responding to prompts, guide students through complex topics step by step (Knowledge Outcome 2,3 & Skill Outcome 3). Practical tasks, such as comparing AI methodologies or identifying biases, help students evaluate tools and systems effectively (Knowledge Outcomes 3,4 & Skill Outcomes 2,4).
Independent study will allow students to extend their knowledge (Knowledge Outcomes 1-4) and prepare for assessments through consolidation of module content, reading of books, journal articles and other recommended references.
Workshops will provide skills training and potentially assessment guidance. Workshops foster active learning, collaboration, and critical thinking (Skill outcome 2), It bridges theory and practice by engaging students in problem-solving, discussions (Skill outcome 4), providing practice in analytical thinking and problem-solving, and hands-on activities such as interpreting datasets (Skill outcomes 1,3) or exploring AI tools. This enhances understanding and retention while developing teamwork and communication skills (Skill outcome 5). Facilitators can guide students in real-time, clarifying foundational ideas and linking to practical applications
Formative assessments will provide feedback and practice for final group projects and open book assessment.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 120 | 2 | A | 50 | Open book written exam 4 questions set 1 to be answered |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Poster | 1 | M | 50 | 10 mins for each student to present a poster to a group of peers and 5 mins to answer questions. Peers will assess the poster. |
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 |
|---|---|---|---|
| Design/Creative proj | 1 | M | Presentation, video or graphical abstract (as per students choice). Short presentation of slide(s) graphical abstract or video within the context of an interactive workshop. Coupled with short round of peer and instructor feedback. |
Assessment Rationale And Relationship
Open book exam essay will test understanding of critical concepts and methods and ability to martial and evaluate these to synthesise responses to questions (skill outcomes 2, 3) (knowledge outcomes 1,2).
Poster presentation this assesses a students’ ability to consolidate and integrate their knowledge and understanding of the topic via a visually appealing platform, whereby Students propose an AI-based solution to a biomedical problem following on from workshops, lectures and small group teaching (Skill outcomes 1, 4, 5) (Knowledge Outcomes 3, 4)
Formative short presentation using slides, video or graphical abstract on the role of AI/ML in shaping the future of precision medicines as a prelude to the summative coursework assessment all done within the context of an interactive workshop. This should help to consolidate understanding of the subject area but also begin to explore issues which will form the basis of the summative coursework assessment and an opportunity to receive feedback on effective communication (skills outcomes 1,2,5) (Knowledge outcomes 1,2)
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- BMD3021's Timetable