Machine learning to predict blood function – towards better haematology analysis

Blood represents a complex mixture of blood cells, plasma proteins, hormones, lipids, salts and water. Blood is good indicator of health, and clinically, blood samples are taken frequently to monitor health, risk of disease or success of treatments. Almost all blood samples will be examined for cellular composition - which is often referred to as a full blood count. This may be achieved using a haematology analyser which counts cells of different sizes and also measures haemoglobin levels using fluidics, optical techniques or impedance properties of each cell type. Other types of analysis are used to measure the amount of chemicals that are present in the blood which can also be useful in diagnosis or disease prediction. What none of these tests do is ask how well blood cells, do their jobs. This is because this is technically demanding and requires specialist equipment. So, while knowing how well our blood cells work could be clinically very valuable, some more sophisticated analysis techniques frequently do not make it beyond the research lab.

Within the Platelet Biology Group within the School of Biological Sciences, we focus on understanding how platelet function is controlled and this may go wrong and trigger heart attacks and strokes. We perform many tests that could be used to assess the risk of disease or to guide therapies, but many are just too complicated for use in routine hospital labs. Using these sophisticated approaches, we will investigate whether simpler tests may predict more sophisticated analysis results. This would help us to determine more useful information from blood samples without the need for complex analysis. 

This PhD project aims to develop approaches that employ Machine Learning techniques to explore the relationships between blood parameters and platelet function. The PhD research goal would be to design a simple and accessible test that could be used by non-experts to analyse blood function. In addition to the development of image analysis, cytometry and machine learning approaches, there would be the opportunity to help design new technical approaches towards this goal. Previous experience with blood research will not be required but could be helpful. The PhD project will involve the use of large existing datasets, and the acquisition and testing of new samples.

School of Biological Sciences, University of Reading:

The University of Reading, located west of London, England, provides world-class research education programs. The University’s beautiful Whiteknights Campus a 30-minute train ride to central London and 40 minutes from London Heathrow airport. 

Our School of Biological Sciences conducts high-impact research, tackling current global challenges faced by society and the planet ranging from health and disease to new ways to protect the natural world. In 2020, we moved into a stunning new ~£60 million Health & Life Sciences building. This state-of-the-art facility is purpose-built for science research and teaching.

Within the School you will join a vibrant community of ~180 PhD students representing ~40 nationalities. Our students publish in high-impact journals, present at international conferences, and organise a range of exciting outreach and public engagement activities.

During your PhD, you will expand your research knowledge and skills, receiving supervision in one-to-one and small group sessions. You will have access to cutting-edge technology and learn the latest research techniques, alongside transferable skills that will support your career aspirations.

The University of Reading is a welcoming community for people of all faiths and cultures. We are committed to a healthy work-life balance and will work to ensure that you are supported personally and academically.

Eligibility:

Applicants should have a good degree (minimum of a UK Upper Second (2:1) undergraduate degree or equivalent) in an area related to the topic of this PhD. In particular we are interested in applicants with experience in Machine Learning and Data Analysis.   Applicants will also need to meet the University’s English Language requirements. We offer Pre-sessional English courses that can help with meeting these requirements. With a commitment to improving diversity in science and engineering, we encourage applications from underrepresented groups.

How to apply:

Submit an application for a PhD in Biomedical

Sciences at http://www.reading.ac.uk/pgapply.

Further information:

http://www.reading.ac.uk/biologicalsciences/SchoolofBiologicalSciences/PhD/sbs-phd.aspx

Enquiries:

Professor William Holderbaum, email: w.holderbaum@reading.ac.uk[FH5] 

Professor Jon Gibbins email: j.m.gibbins@reading.ac.uk