Title: SCH09: Epigenetic machine learning: utilizing DNA methylation patterns to predict age acceleration
Funding: Full time Home/EU fees and a stipend of £15,009 p.a. (terms & conditions)
Application deadline: 7 May 2019
Start date: October 2019
Duration: 3 years (full time)
Location: Colchester Campus
Based in: School of Computer Science and Electronic Engineering (in collaboration with the School of Biological Sciences)
This studentship is now closed to applicants. View our latest opportunities.
This highly interdisciplinary studentship brings together the expertise of the Genomics and Computational Biology Group with the Embedded and Intelligent Systems Laboratory, also leveraging data from the UK Household Longitudinal Study held from Institute for Social and Economic Research (ISER), which could in turn enable a significant scientific impact potential for this timely research endeavour.
Therefore, we are looking for a highly motivated and interdisciplinary minded student, who has an excellent computer science, bioinformatics, electronics or related UG/MSc degree and is keen to work on relevant research in the area of design deep learning architectures and algorithms for age prediction based on DNA methylation data.
Aging and health is a supremely timely topic as life expectancies worldwide are higher than ever and non-communicable causes of death now predominate for a majority of the world's population.
DNA methylation profiles are of great interest because they are an epigenetic measure related to gene expression and its regulation but more stable and easier to analyse than gene expression itself or other epigenetic marks.
One topic of particularly wide interest currently is epigenetic change with age, both as a way of accurately aging unknown DNA samples and as a way of understanding some aspects of biological aging and health and environmental effects on it. Deep learning-based machine learning technologies have demonstrated their increasing usefulness as a powerful tool in pattern recognition and demonstrated outstanding performance (predominantly outperforming traditional non-artificial intelligence bases approaches) in visual object identification, speech recognition, biomedical signal analysis and other fields requiring hierarchical analysis of input data.
Inspired by these examples, the aim of this project is to explore if deep convolutional neural networks (CNNs) architectures can be employed to generate similarly spectacular results for age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age and tissue classification using data from blood samples.
The main anticipated outcome for this project is that the proposed deep CNN architecture would simultaneously and automatically producing the set of features engineered from the raw DNA methylation data and bring age prediction and tissue classification to a new level, which could be potentially used with a combination of wearable sensors and deep learning technologies for Health Risks Assessment (HRA) applications involving continuous health risk evaluation and real-time feedback to patients and care providers in future.
The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.
Lead supervisorSchool of Computer Science and Electronic Engineering, University of Essex
Dr Xiaojun Zhai is a Lecturer in the Embedded Intelligent Systems Laboratory at the University of Essex, he has been part of the EPSRC or EU funded projects for developing Embedded System and System-on-Chip (SoC) solutions for healthcare, machine learning and pattern recognition applications as well as other real-time critical applications. His research interests include: machine learning, embedded systems, high performance reconfigurable computing, hardware/software codesign, connected health applications, image and video processing applications.
Co-supervisorSchool of Computer Science and Electronic Engineering, University of Essex
Klaus D. McDonald-Maier is currently the Head of the Embedded and Intelligent Systems Laboratory, University of Essex. He is also the Chief Scientist with UltraSoC Technologies Ltd., the CEO of Metrarc Ltd., and a Visiting Professor with the University of Kent. His current research interests include embedded systems and system-on-chip design, security, development support and technology, parallel and energy-efficient architectures, computer vision, data analytics, and the application of soft computing and image processing techniques for real-world problems.
Co-supervisorSchool of Biological Sciences, University of Essex
Professor Leonard Schalkwyk is the head of the Genomics group and the Director of Research for the School of Biological Sciences, University of Essex. He has a long track record of methodological development in genomics and bioinformatics, and of funding from national and international sources. He is currently Co-I on an MRC grant with J. Mill (Exeter) ''Regulatory genomic profiling in schizophrenia", and involved with an ESRC-funded DNA methylation profiling study of 1200 individuals from the UK Household Longitudinal Study.
You can apply for this postgraduate research opportunity online.
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The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.
Instruction to applicants
When you apply online you will be prompted to fill out several boxes in the form:
Without this information the application may not get to the correct supervisors.
If you have any informal queries about this opportunity please email the lead supervisor, Dr Xiaojun Zhai (firstname.lastname@example.org).
You can find the terms and conditions of this studentship here (PDF).