• University of Macau Featured PhD Programmes
  • Queen’s University Belfast Featured PhD Programmes
  • Northumbria University Featured PhD Programmes
  • University of Birmingham Featured PhD Programmes
  • University of Stirling Featured PhD Programmes
  • University of Exeter Featured PhD Programmes
  • University of Manchester Featured PhD Programmes
  • University of Warwick Featured PhD Programmes
University of Dundee Featured PhD Programmes
University of Kent Featured PhD Programmes
Aberdeen University Featured PhD Programmes
Coventry University Featured PhD Programmes
Lancaster University Featured PhD Programmes

A Machine Learning Approach to Automatic Inference of Biological Pathways

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr W Pang
    Prof G M Coghill
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Understanding biological processes at a system level remains constantly challenging, even with the recent development of biological techniques (e.g. RNA-Seq, high-throughout DNA sequencing), which make massive amounts of experimental data available, the so-called ‘omics’ data. Systems biology aims to address the challenge by studying biological systems with mathematical/computational models, for instance, ordinary differential equations, qualitative differential equations, and Boolean networks.

One fundamental task in systems biology is to construct models based on available data and domain knowledge. Much effort has been made to manually construct the models with modellers’ own experience and knowledge.

However, it is often the case that there may be many candidate models given existing knowledge and data. There may also exist hidden components that are not identified in a biological system. All of the above require automatic approaches to generating and testing candidate models in a systematic and effective way.

Machine learning offers such automatic approaches: it can infer the most possible models from incomplete knowledge and limited data, and this can greatly benefit modellers and biologists for further research.

In this project you will study the following topics:

1. How to build a machine learning platform which can automatically generate and test biological models.
2. How to effective search possible models from the potentially very large search space.
3. How can we interact with the biologists so that the platform can be used to real biological problems.

This project will involve the collaboration with a cancer biologist (Dr. Adrian Saurin) based at Dundee, and regular visits to Dundee will be required.

The successful candidate should have, or expect to have, an Honours Degree at 2.1 or above (or equivalent) in Computing Science, Applied Mathematics, Systems Biology, Bioinformatics, and other related disciplines.

Knowledge of: Essential: machine learning basics; programming in Java, Python, Ruby, R, or Scala.

Desirable: biology basics; evolutionary computing; ordinary differential equations; numerical simulation.

Funding Notes

There is no funding attached to this project, it is for self-funded students only


This project is advertised in relation to the research areas of the discipline of Computing Science. Formal applications can be completed online: http://www.abdn.ac.uk/postgraduate/apply. You should apply for PhD in Computing Science, to ensure that your application is passed to the correct College for processing. NOTE CLEARLY THE NAME OF THE SUPERVISOR and EXACT PROJECT TITLE ON THE APPLICATION FORM. Applicants are limited to applying for a maximum of 2 projects. Any further applications received will be automatically withdrawn.

Informal inquiries can be made to Dr W Pang ([email protected]) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Graduate School Admissions Unit ([email protected]).

Cookie Policy    X