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Curricular information is subject to change
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
• 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 Type | Hours |
---|---|
Lectures | 24 |
Computer Aided Lab | 24 |
Autonomous Student Learning | 152 |
Total | 200 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Essay: Self-assessment essay | Varies over the Trimester | n/a | Graded | No | 20 |
Presentation: Group work: modeling project | Varies over the Trimester | n/a | Graded | No | 30 |
Assignment: Write modelling proposal | Varies over the Trimester | n/a | Graded | No | 30 |
Assignment: Machine learning mini project | Varies over the Trimester | n/a | Graded | No | 20 |
Remediation Type | Remediation Timing |
---|---|
In-Module Resit | Prior to relevant Programme Exam Board |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities
• Self-assessment activities
The class will receive feedback thRough discussions in the class Students will receive individual feedback 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 |