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:


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.

Professor Aaron Quigley new SICSA Director

Congratulations to Professor Aaron Quigley who has been appointed as the new Director of SICSA. Aaron, the Chair of Human Computer Interaction co-founded SACHI, the St Andrews Computer Human Interaction research group and served as its director from 2011-2018.

In his volunteer roles he is the ACM SIGCHI Vice President for Conferences (on the ACM SIGCHI Executive Committee), member of the ACM Europe Council Conferences Working Group, a board member of ScotlandIS and an ACM Distinguished Speaker. Aaron will be general co-chair for the ACM CHI conference in Asia in 2021.

For more information about Professor Quigley, please see

Juho Rousu: Predicting Drug Interactions with Kernel Methods

Event details

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

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.

PhD viva success: Evan Brown

Congratulations to Evan Brown, who successfully defended his thesis today. He is pictured with Internal examiner Dr Tristan Henderson and external examiner Professor Chris Marsden, Professor of Internet Law at the University of Sussex.

Evan’s PhD research on using corpus linguistics to build collaborative legal research tools was supervised by Professor Aaron Quigley.

Continued success for MSc student Jessica Cooper

The work of our MSc student, Jessica Cooper, supervised by Oggie Arandjelovic on the use of deep learning for the analysis of ancient Roman coins has been attracting widespread attention. From tech media to web sites of history, heritage, and numismatics focused communities, Jessica’s work has been recognized as highly innovative, with a potential to change the direction of research in the area. Jessica will be rejoining St Andrews in a month’s time, working with Oggie Arandjelovic on deep learning in pathology image analysis.

Best paper finalist award for Xingzhi Yue and Neofytos Dimitriou

A paper describing the work of our MSc student Xingzhi Yue and PhD student Neofytos Dimitriou, supervised by Oggie Arandjelovic and in collaboration with the School of Medicine, gets the best paper finalist award at the latest International Conference on Bioinformatics and Computational Biology (BICOB 2019). The key contribution of the work is a novel deep learning based algorithm for the analysis of extremely large pathology image slides, capable of automating and improving colorectal cancer prognosis.

Distinguished Speaker: Australia, Columbia and Thailand

This Saturday Professor Aaron Quigley will deliver a keynote talk on Global Human Computer Interaction at the Thai SIGCHI Symposium in Bangkok. This is the first symposium of the Bangkok ACM SIGCHI Chapter which aims to connect the Thai UX and HCI communities together with those beyond their borders. This talk is part of the Distinguished Speaker Program (DSP) of the Association for Computing Machinery (ACM).

In May, Professor Quigley will travel to Melbourne and Sydney Australia as part of the ACM DSP program. First, he will deliver a talk on the Future of Interaction at the Melbourne Knowledge Week followed by a “fireside chat” and panel in the University of Melbourne and finally a seminar in the University of Sydney. His talks will cover a number of areas of research he explores with his colleagues and students in SACHI, the St Andrews Computer Human Interaction research group.

In August, Aaron has been invited to deliver a keynote at the 5th Workshop on ICTs for improving Patients Rehabilitation Research Techniques in Popayán, Colombia. This talk will focus on some of Aaron’s more recent, and unpublished research, in augmenting interactions in AR and his older work on technology for rehabilitation and older people.

Professor Quigley is currently on sabbatical in the National University of Singapore but he will attend the CHI 2019 conference in Glasgow this May with SACHI colleagues and graduate students presenting their latest research.

Encoding Egyptian quadrats in Unicode

Unicode 12, released 5th March 2019, includes 9 control characters for Ancient Egyptian hieroglyphic text. These resulted from an initiative by Dr. Mark-Jan Nederhof (St Andrews) and Egyptologists at the University of Liège, CNAM (Paris) and the Berlin-Brandenburgische Akademie der Wissenschaften, in collaboration with Unicode experts. The control characters allow hieroglyphs to be arranged horizontally and vertically much as in original inscriptions. This removes the foremost obstacle to adoption of Unicode in Egyptology.

The control characters:

Although existing fonts are not yet able to interpret the control characters directly, hieroglyphic text can now be displayed on web pages with the help of JavaScript:

Lao Characters for Pali added to Unicode 12

Congratulations to Vinodh Rajan, Ben Mitchell, Martin Jansche and Sascha Brawer on their successful proposal for additions to the repertoire of ISO/IEC 10646, which will see Pali letters added to Lao in Unicode 12. As a result, it is now possible to write both Pali/Sanskrit in Lao and represent the entire Tripitaka in the Lao script. The proposal ( submitted in 2017 was finally added to the Unicode standard this year.

Vinodh explained that the proposal allows four things. Firstly, one can now transcribe liturgical Pali (the liturgical language of Theravada Buddhism) texts and by extension the whole Pali Tripitaka (the Theravada Buddhist canon) in the Lao script without any distortion, providing lay people accurate access to these liturgical texts. Previously, the texts had to go through some sort of distortion due to the lack of appropriate characters, which means they had to be approximated. Secondly, it allows people who would want to use etymological orthography for Lao (it currently uses a phonemic orthography) access to the necessary additional characters. Thirdly, there are several books printed (mostly in the 1930’s) using the expanded alphabet that need to be eventually digitized. This will enable their proper digitization by allow plain-text representation of all the Lao characters. Lastly, it will improve the transliteration accuracy between Lao and neighboring scripts like Thai and Khmer.

The expanded Lao alphabet can be found here:

Vinodh, a St Andrews Computer Science alumnus completed his PhD in 2016. His thesis, Quantifying scribal behavior : a novel approach to digital paleography was supervised by Dr Mark-Jan Nederhof.