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The Edinburgh Mouse Atlas aims to capture in-situ gene expression patterns in a common spatial framework. In this study, we construct a grammar to define spatial regions by combinations of these patterns. Combinations are formed by applying operators to curated gene expression patterns from the atlas, thereby resembling gene interactions in a spatial context. The space of combinations is searched using an evolutionary algorithm with the objective of finding the best match to a given target pattern. We evaluate the method by testing its robustness and the statistical significance of the results it finds.

Workflow technologies are emerging as the dominant approach to coordinate groups of distributed services. However with a space filled with competing specifications, standards and frameworks from multiple domains, choosing the right tool for the job is not always a straightforward task. Researchers are often unaware of the range of technology that already exists and focus on implementing yet another proprietary workflow system. As an antidote to this common problem, this paper presents a concise survey of existing workflow technology from the business and scientific domain and makes a number of key suggestions towards the future development of scientific workflow systems.

Evolutionary computation involves the study of problem-solving and optimization techniques inspired by principles of evolution and genetics. As any other scientific field, its success relies on the continuity provided by new researchers joining the field to help it progress. One of the most important sources for new researchers is the next generation of PhD students that are actively studying a topic relevant to this field. It is from this main observation the idea arose of providing a platform exclusively for PhD students.

Metaheuristics have shown to be effective for difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization and estimation of distribution algorithms. Problems solved successfully include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satisfiability and general mixed integer programming. EvoCOP began in 2001 and has been held annually since then. It is the first event specifically dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems. Originally held as a workshop, EvoCOP became a conference in 2004. The events gave researchers an excellent opportunity to present their latest research and to discuss current developments and applications. Following the general trend of hybrid metaheuristics and diminishing boundaries between the different classes of metaheuristics, EvoCOP has broadened its scope over the last years and invited submissions on any kind of metaheuristic for combinatorial optimization.

Graph vertex colouring can be defined in such a way where colour assignments are substituted by vertex contractions. We present various hyper-graph representations for the graph colouring problem all based on the approach where vertices are merged into groups. In this paper, we show this provides a uniform and compact way to define algorithms, both of a complete or a heuristic nature. Moreover, the representation provides information useful to guide algorithms during their search. In this paper we focus on the quality of solutions obtained by graph colouring heuristics that make use of higher order properties derived during the search. An evolutionary algorithm is used to search permutations of possible merge orderings.

You have stumbled upon my hyperhome, welcome! Me being a scientist, this page is focused on my work. I currently lead a research group at the National e-Science Centre of the University of Edinburgh. If you are not into science, you might enjoy some of my photographs or you could take a look at some of my current or past projects.

We aim to build a portlet that allows developers and advanced users to create and deploy quickly job submission portlets. The portlet builder should allow specification of the parameters to their applications, and even allow parameter sweeps to be set up so as to do their in-silico experiments and sensitivity analyses. Job submission portlets will be deployed dynamically in the portal. Such a tool fits well in OMII-UK's portal support call, as it would speed up the deployment of portals that provide an interface to applications of end-users.

Research into animal and human health covers a vast array of biological components and functions. Yet strategies to simulate biological systems across multiple levels, by integrating many components and modelling their interaction, are largely undeveloped. We will explore how this challenge can be approached by considering how to build a virtual fly brain. This offers a new proving ground for collaboration between e-Scientists, biologists and neuroinformaticists. Mental Health accounts for 11% of global disease burden, it is growing rapidly yet it is one of the most challenging areas for drug discovery and development. Realistic models that capture the processes of the human brain would provide new insights into the diagnosis and treatment of certain disorders. However, to achieve this, we need to begin by working from much simpler models. The brain of the Drosophila contains in the region of 100,000 neurons; it provide perhaps the simplest brain capable of what we would consider complex behaviour, much of which offers insight into animal and human cognition. The genome was sequenced in 2000 and efforts to improve its functional annotation are highly integrated (www.flybase.org). Of the estimated 12,000 Drosophila genes, more than 2,000 are conserved in human disease indications. In order to bring together the many disciplines, the e-Science Institute of the UK has sponsored a theme to allow the establishment of programme with a point of focus for bioinformatics and neuroinformatics in Drosophila, such that gaps in the current databases, biological domain and modelling/simulation efforts can be identified and translated into new projects. In the context of e-Science, the project shall serve as a testbed for the new service oriented platform to enable a distributed data integration and data mining infrastructure, which will be developed in a European project.

We introduce the Edinburgh Mouse Atlas and show several examples of the analyses now available to biologists on the spatio-temporal in-situ gene expression data. Then we move on to more advanced methodologies for extracting knowledge from these data.

A new top down approach to extracting knowledge from the spatio-temporal atlas of gene expression patterns in the developing mouse embryo.

We analyse data from the Edinburgh Mouse Atlas Gene-Expression Database (EMAGE) which is a high quality data source for spatio-temporal gene expression patterns. Using a novel process whereby generated patterns are used to probe spatially-mapped gene expression domains, we are able to get unbiased results as opposed to using annotations based predefined anatomy regions. We describe two processes to form association rules based on spatial configurations, one that associates spatial regions, the other associates genes.

An overview of e-Science and the activities at the Edinburgh National e-Science Centre.