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MEIN40330

Academic Year 2024/2025

AI for Personalised Medicine (MEIN40330)

Subject:
Medical Informatics
College:
Health & Agricultural Sciences
School:
Medicine
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr Oleksii Rukhlenko
Trimester:
Summer
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module teaches computational methods (with a focus on AI and ML) for generating personalised disease models and patient models. The module has a special focus on pathway and network approaches for stratifying patients and predicting clinical outcomes. It will cover, supervised and unsupervised machine learning techniques for analysing ~omics patient data, including genomic, transcriptomic and proteomics data, and dynamic modelling of disease and drug response mechanisms for the construction of personalised models. Lectures will introduce the concepts and required mathematics, but most of the learning will be delivered through student-led, problem-based activities. This module forms part of the core curriculum for the MSc AI for Medicine and Medical Research.

About this Module

Learning Outcomes:

After successful completion of this module, the student will be able to
• Interpret and model various types of ~omics data from patients, including genomic, transcriptomic and proteomics data
• Apply unsupervised and supervised machine learning to stratify patients and predict clinical outcomes
• Formulate strategies for building, calibrating and validating personalised models
• Construct personalised models to gain insight into disease and drug response mechanism
• Appraise machine learning and dynamic modelling approaches for personalised medicine

Indicative Module Content:

• Unsupervised and supervised learning of patients ~omics data
• Pathway and network analysis of biomolecular data
• Survival analysis
• Dynamic modelling of intracellular disease networks

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

152
Lectures

24
Computer Aided Lab

24
Total

200

Approaches to Teaching and Learning:
1) Problem based learning
2) Lectures
3) Tutorials

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Machine learning mini-project. Students must demonstrate that they have basic knowledge and skills of survival analysis, i.e. Cox regression analysis of clinical, mutation and expression data. Week 4 Graded Yes
25
 Yes
Assignment(Including Essay): Modelling project. Students must demonstrate their ability to apply ODE models to stratify patients into groups of good and bad prognosis. Week 7 Graded Yes
25
 Yes
Group Work Assignment: Final project. Group of students must show their ability to apply different ML and modelling techniques and analyze different data modalities to build predictive models of treatment outcome. Week 10 Graded Yes
50
 Yes
Reflective Assignment: Self-assessment assay, not mandatory, not graded. Week 10 Other No
0
 No

Carry forward of passed components
No
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities
• Self-assessment activities

How will my Feedback be Delivered?

The class will receive feedback through discussions in the class. Students will receive individual feedback during practical sessions and on their assignments through the VLE system. Feedback on self-assessment. Students will receive feedback on their presentation by their peers.

Name Role
Dr Vadim Zhernovkov Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Summer Lecture Offering 51 Week(s) - 37, 38, 39, 40, 41, 42, 43, 44, 45, 46 Tues 10:00 - 18:50
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