TY  - CONF
T1  - Matching Spatial Regions with Combinations of Interacting Gene Expression Patterns
T2  - Communications in Computer and Information Science
Y1  - 2008
A1  - van Hemert, J. I.
A1  - Baldock, R. A.
ED  - M. Elloumi
ED  - \emph
ED  - et al
KW  - biomedical
KW  - data mining
KW  - DGEMap
KW  - e-Science
AB  - 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.
JF  - Communications in Computer and Information Science
PB  - Springer Verlag
ER  - 

TY  - CONF
T1  - Mining spatial gene expression data for association rules
T2  - Lecture Notes in Bioinformatics
Y1  - 2007
A1  - van Hemert, J. I.
A1  - Baldock, R. A.
ED  - S. Hochreiter
ED  - R. Wagner
KW  - biomedical
KW  - data mining
KW  - DGEMap
KW  - e-Science
AB  - 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.
JF  - Lecture Notes in Bioinformatics
PB  - Springer Verlag
UR  - http://dx.doi.org/10.1007/978-3-540-71233-6_6
ER  -