Health Informatics Group

Welcome

This is the website for the Health Informatics Group within the School of Computer Science at the University of St Andrews. Find out who our members are on the People page. Have a look at some of our Research Projects. Or take a look at all the papers published by our members on the Publications page.

HIG focuses on developing biomedical models, hypotheses, and systems for future biomedical research projects using data from both studies and simulations.

News and Events

The latest Health Informatics Group posts from the School of Computer Science blog.

St Andrews Bioinformatics Workshop 10/06/19


Event details

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

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:

Agenda:

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.


Juho Rousu: Predicting Drug Interactions with Kernel Methods


Event details

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

Title:
Predicting Drug Interactions with Kernel Methods

Abstract:
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.

References:
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, https://doi.org/10.1093/bioinformatics/bty277

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.


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.