World-Leading PhD Scholarship in Health Informatics

A fully-funded PhD scholarship is available to support an exceptional student wishing to undertake doctoral research in health informatics, in particular looking at analysing and predicting disease trajectories of multimorbidity. This prestigious PhD scholarship is awarded by St Leonard’s Postgraduate College at the University of St Andrews and will be supervised by Dr Areti Manataki, Dr Katherine Keenan, Prof Colin McCowan and Dr Michail Papathomas. Applications must be received by 12 June 2023.

Further information, including how to apply, can be found at:

The Serums Project Consortium meeting

This Week Dr Juliana Bowles brought together nine leading academic and industry partners for the 4th Consortium meeting for the Serums project.

The project aims to produce tools and technologies to support future-generation healthcare systems that will integrate home-based healthcare into a holistic treatment plan, reducing cost and travel-associated risks and increasing quality of healthcare provision.

For further information on the project visit the Serums website

Image and text provided by Annemarie Paton

The Melville Trust for the Care and Cure of Cancer PhD award

The Melville Trust for the Care and Cure of Cancer have funded a PGR Studentship relative to the project entitled ‘Detecting high-risk smokers in Primary Care Electronic Health Records: An automatic classification, data extraction and predictive modelling approach’.

The supervisors are Prof. Frank Sullivan of the School of Medicine and Prof. Tom Kelsey of the School of Computer Science, with work commencing in September 2019. The award is for £83,875.

St Andrews Bioinformatics Workshop 10/06/19

Next Monday is the annual St Andrews Bioinformatics workshop in Seminar Room 1, School of Medicine. Some of the presentations are very relevant to Computer Science, and all should be interesting. More information below:


14:00  – 14:15: Valeria MontanoThe PreNeolithic evolutionary history of human genetic resistance to Plasmodium falciparum

14:15 – 14:30: Chloe Hequet: Estimation of Polygenic Risk with Machine Learning

14:30 – 14:45: Roopam Gupta: Label-free optical hemogram of granulocytes enhanced by artificial neural networks

15:00 – 15:15: Damilola Oresegun: Nanopore: Research; then, now and the future

15:15 – 15:30: Xiao Zhang: Functional and population genomics of extremely rapid evolution in Hawaiian crickets

15:30 – 16:00: Networking with refreshments

16:00 – 17:00: Chris Ponting: The power of One: Single variants, single factors, single cells

You can register your interest in attending here.

Event details

  • When: 10th June 2019 14:00 - 17:00
  • Format: Lecture, Talk, Workshop

Juho Rousu: Predicting Drug Interactions with Kernel Methods

Predicting Drug Interactions with Kernel Methods

Many real world prediction problems can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs. We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem.

Anna Cichonska, Tapio Pahikkala, Sandor Szedmak, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu; Learning with multiple pairwise kernels for drug bioactivity prediction, Bioinformatics, Volume 34, Issue 13, 1 July 2018, Pages i509–i518,

Short Bio:
Juho Rousu is a Professor of Computer Science at Aalto University, Finland. Rousu obtained his PhD in 2001 form University of Helsinki, while working at VTT Technical Centre of Finland. In 2003-2005 he was a Marie Curie Fellow at Royal Holloway University of London. In 2005-2011 he held Lecturer and Professor positions at University of Helsinki, before moving to Aalto University in 2012 where he leads a research group on Kernel Methods, Pattern Analysis and Computational Metabolomics (KEPACO). Rousu’s main research interest is in learning with multiple and structured targets, multiple views and ensembles, with methodological emphasis in regularised learning, kernels and sparsity, as well as efficient convex/non-convex optimisation methods. His applications of interest include metabolomics, biomedicine, pharmacology and synthetic biology.

Event details

  • When: 30th April 2019 14:00 - 15:00
  • Where: Cole 1.33a
  • Format: Seminar

Tom Kelsey appointed Associate Editor of Human Reproduction Update

Arne Sunde, the incoming Editor-in-Chief, has appointed Tom Kelsey as Associate Editor of Human Reproduction Update.

Human Reproduction Update is the leading journal in Reproductive Medicine, with an Impact Factor of 11.852. The journal publishes comprehensive and systematic review articles in human reproductive physiology and medicine, and is published on behalf of the European Society of Human Reproduction and Embryology (ESHRE). The Associate Editor system at Human Reproduction Update has been in place since the beginning of 2001 and it has a significant positive effect on the quality and dynamism of the journal.

In the ISI JCR Global Ranking for 2017, Human Reproduction Update is ranked first of 29 journals in Reproductive Biology, and first of 82 journals in Obstetrics & Gynecology.

Tom Kelsey has published extensively in Human Reproduction Update and its sister journals Human Reproduction (impact factor 4.949) and Molecular Human Reproduction (impact factor 3.449). He is also Associate Editor for the Open Access journals Frontiers in Endocrinology and Frontiers in Physiology. He is a regular reviewer for these journals and also the British Medical Journal, BMJ Open, Health Education Journal, Nature Scientific Reports, PLOS One, Mathematical Medicine and Biology, Systems Biology in Reproductive Medicine, and the European Journal of Obstetrics & Gynecology and Reproductive Biology.

Job vacancies: Interdisciplinary Data Scientists

The Schools of Medicine and Computer Science are seeking to appoint three highly motivated data scientists with a passion for computer vision and deep learning, and specifically their application to medical imaging. The data scientists will be based in the Schools of Computer Science and Medicine at the University of St Andrews and will work on a national Innovate UK funded initiative to create a pan Scotland Industrial Centre for AI Research in Digital Diagnostics (iCAIRD).

The successful candidates will have the opportunity to work alongside and learn from clinicians, industrial experts from Philips Healthcare and academics to help develop artificial intelligence solutions for the automatic reporting of cancer diagnoses in endometrial and cervical cancer. The main duties of the role will involve being an active member of an interdisciplinary team of scientists to help develop deep learning algorithms, within industry standard guidelines, to analyse patient samples in a manner that allows rapid clinical transfer. This work will therefore have the opportunity to impact both patient welfare and relieve pathologist work burden.

Applicants should have experience in machine learning, demonstrable experience in computer programming languages and an interest in the medical applications of computer science. The candidates would benefit from a track record in scientific writing and working in interdisciplinary teams as well as experience in computer vision.

The posts are full time and over a period of 36 months.
Closing Date: 18 January 2019

Find out more about the vacancies further particulars on the recruitment website.

Computational Approaches for Accurate, Automated and Safe Cancer Care – HIG Seminar

Modern external beam radiation therapy techniques allow the design of highly conformal radiation treatment plans that permit high doses of ionsing radition to be delivered to the tumour in order to eradicate cancer cells while sparing surrounding normal tissue. However, since it is difficult to avoid irradiation of normal tissue altogether and ionising radiation also damages normal cells, patients may develop radiation-induced toxicity following treatment. Furthermore, the highly conformal nature of the radiation treatment plans makes them particularly susceptible to geometric or targeting uncertainties in treatment delivery. Geometric uncertainties may result in under-dosage of the tumour leading to local tumour recurrence or unacceptable morbidity from over-dosage of neighbouring healthy tissue.

I will present work in three areas that bear directly on treatment accuracy and safety in radiation oncology. The first area addresses the development of automated image registration algorithms for image-guided radiation therapy with the aim of improving the accuracy and precision of treatment delivery. The registration methods I will present are based on statistical and spectral models of signal and noise in CT and x-ray images. The second part of my talk addresses the identification of predictors of normal tissue toxicity after radiation therapy and the study of the spatial sensitivity of normal tissue to dose. I will address the development of innovative methods to accurately model the spatial characteristics of radiation dose distributions in 3D and results of the analysis of this important, but heretofore lacking, information as a contributing factor in the development of radiation-induced toxicity. Finally, given the increasing complexity of modern radiation treatment plans and a trend towards an escalation in prescribed doses, it is important to implement a safety system to reduce the risk of adverse events arising during treatment and improve clinical efficiency. I will describe ongoing efforts to formalise and automate quality assurance processes in radiation oncology.

Reshma Munbodh is currently an Assistant Professor in the Department of Diagnostic Imaging and Therapeutics at UConn Health. She received her undergraduate degree in Computer Science and Electronics from the University of Edinburgh and her PhD in medical image processing and analysis applied to cancer from Yale University. Following her PhD, she performed research and underwent clinical training in Therapeutic Medical Physics at the Memorial Sloan-Kettering Cancer Center. She is interested in the development and application of powerful analytical and computational approaches towards improving the diagnosis, understanding and treatment of cancer. Her current projects include the development of image registration algorithms for image-guided radiation therapy, the study of normal tissue toxicity following radiation therapy, longitudinal studies of brain gliomas to monitor tumour progression and treatment response using quantitative MRI analysis and the formalisation and automation of quality assurance processes in radiation oncology.

Event details

  • When: 22nd November 2017 14:00 - 15:00
  • Where: Cole 1.33a
  • Series: HIG Seminar Series
  • Format: Seminar

DHSI Seminar Series (Digital Health Science Initiative)


Seminar Room 1 School of Medicine

12:00: Alex Baldacchino- Introduction

12:15: Ognjen Arandjelović & Aniqa Aslam- Understanding Fatal and Non-Fatal Drug Overdose Risk Factors in Fife: Overdose Risk (OdRi) tool

12:45: Damien Williams & Fergus Neville- Transdermal alcohol monitoring

13:15: David Harris-Birtill & David Morrison- Narco Cat – waste water analysis in substance misuse – a novel epidemiological tool

13:15 – 14.00: All Questions & Opportunities

Event details

  • When: 14th June 2017 12:00 - 14:00
  • Where: N Haugh, St Andrews
  • Format: Seminar

Computational Models of Tuberculosis

On 10th February, Michael Pitcher gave a talk on his upcoming work for his PhD.

Michael is a first-year PhD student based in the School of Computer Science, whose research also involves close collaboration with the School of Medicine. Michael’s work involves investigation of the use of computational models to simulate the progression and treatment of tuberculosis within individuals.
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