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CS904 Computational Biology

CS904 15 CATS Term 2


Option - MSc Computer Science, MEng Computer Science

Academic Aims

The module is designed to develop student research skills in the broad area of computational biology. It will cover topics on the analysis of massive amounts of data generated in biomedical sciences, in particular DNA/RNA sequences and large multi-gigapixel pathology images. Students will be introduced to the foundations of two fundamental types of biomedical data including genomic sequences and tissue images and how these are acquired and processed. Machine learning plays a central role in processing these data, and to develop computational models that help us better understand the complex phenomena underpinning biological processes. The module will be taught following an “algorithmic approach”, demonstrating that computational biology is a wide-open arena that offers a very diverse range of problems and thus a diverse range of algorithms, making it an exciting and rapidly evolving field for computer scientists.

Learning Outcomes

By the end of the module the student should:

  • Have a basic grasp of fundamental molecular biology concepts as relevant to this module
  • Understand some basic and commonly used algorithms in bioinformatics
  • Know algorithms to compute sequence alignments and how these are applied in current research
  • Understand principles of fluorescence microscopy and image-based modelling of single cell dynamics.
  • Have an understanding of how tissue slides are imaged with high throughput
  • Understand basic problems in the processing and analysis of tissue images and some standard solutions to those
  • Be able to apply image analysis and machine learning to real-world computational pathology problems


Molecular biology fundamentals

  • Sequence alignments
  • Introduction to tissue imaging and computational pathology
  • Whole-slide image (WSI) handling and processing
  • Recognising various kinds of cells in cancerous WSIs
  • Advanced research topics in computational biology


M. Zvelebil and J.O. Baum, Understanding Bioinformatics, Garland Science, 2008

  • L. Pantanowitz and A. Parwani, Digital Pathology, ASCP, 2017
  • The Image Processing Handbook, John C. Russ, CRC Press, 7th ed, 2015


Two hour examination (50%), coursework (50%)


20 one-hour lectures plus 10 one-hour seminars