The module will cover topics on the acquisition of and analysis of large-scale data generated in biomedical sciences, particularly DNA/RNA sequences, live cell microscopy and multi-gigapixel pathology images. Students will be introduced to how these data are acquired, modern machine learning methods to process the data, and computational modelling approaches to help us better understand the complex phenomena underpinning biological processes. The module will be taught following an "algorithmic approach," demonstrating that addressing problems in computational biology requires a diverse range of theoretical concepts and algorithms, making it an exciting and rapidly evolving field for computer scientists. The students will learn using deep learning models for analysis of multi-gigapixel images. Therefore, basic understanding of image processing concepts will be required.
The module is designed to develop student research skills in the broad area of computational biology.
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
By the end of the module, students should be able to:
Zvelebil, M., and Baum, J.O., Understanding Bioinformatics. Garland Science, 2008;
Kremling, A., Systems Biology. CRC Press, 2014;
Pantanowitz, L., and Parwani, A., Digital Pathology. ASCP, 2017;
Alberts, B., et al., Essential cell biology: an introduction to the molecular biology of the cell (5/e). Garland, 2008.
Students need to develop and implement algorithms to address questions typically asked in current research projects. For example: Perform sequence analysis of homologous genes and construct phylogenetic trees, generate simulation data of gene regulatory networks and perform dimensionality reduction and clustering, develop models for cellular dynamics, analyze digital pathology images taken from real-world data.
The module will cover a broad range of techniques used in biology, mathematics, and computer science.
By the end of the module, students will have acquired skills in:
Technical - Technological competence and staying current with knowledge
Communication - Verbal, listening, writing, technical communication skills, using different medium for communicating
Critical Thinking - Problem-solving, analysis of possible solutions etc
Creativity - Ability to harnass creative ideas and turn them into tangible and strategic products/solutions
Type | Required |
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Lectures | 20 sessions of 1 hour (13%) |
Supervised practical classes | 10 sessions of 1 hour (7%) |
Private study | 120 hours (80%) |
Total | 150 hours |
Background reading of research papers, working through additional examples and improving coding skills, revision.
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Weighting | Study time | |
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Assignment 1 | 16% | |
This assignment covers the first section of the module, Bioinformatics. |
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Assignment 2 | 17% | |
This assignment covers the first section of the module, Mathematical Modelling of Cellular Dynamics. |
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Assignment 3 | 17% | |
This assignment covers the first section of the module, Computational Pathology. |
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In-person Examination | 50% | |
CS904 examination
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Weighting | Study time | |
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In-person Examination - Resit | 100% | |
CS904 resit examination. Standard calculator allowed.
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Written comments on coursework
This module is Core optional for:
This module is Optional for:
This module is Option list A for:
This module is Option list B for: