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.

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.

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.

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.

DGEMap is a EU-funded design project that aims to create a blueprint for the organisational and collaborative structures, ethical framework, and molecular genetic and informatics technologies necessary for a new research infrastructure which will accelerate an integrated European approach to gene expression in early human development. In this talk I will first introduce the biologists' laboratory processes and existing tools, and then propose areas where Taverna would fit in. Such areas include linking the mouse model with the human model, linking spatial-temporal gene expression data with other data sources, and providing data mining facilities.

DGEMap is a EU-funded design project that aims to create a blueprint for the organisational and collaborative structures, ethical framework, and molecular genetic and informatics technologies necessary for a new research infrastructure which will accelerate an integrated European approach to gene expression in early human development. In this talk I will emphasise the informatics requirements with the goal to find projects that share a common interests. Included topics are visualisation of 2D/3D data, gene expression patterns, collaboration tools, and data mining and analyses, all focused on grid-aware applications.

DGEMap is a EU-funded design project that aims to create a blueprint for the organisational and collaborative structures, ethical framework, and molecular genetic and informatics technologies necessary for a new research infrastructure which will accelerate an integrated European approach to gene expression in early human development. In this talk I will emphasise the informatics requirements with the goal to find projects that share a common interests. Included topics are visualisation of 2D/3D data, gene expression patterns, collaboration tools, and data mining and analyses, all focused on grid-aware applications.