Mining Spatial Gene Expression Data for Association Rules

  • Jano van Hemert
  • Richard Baldock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4414)


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.


association rules gene expression patterns in situ hybridization spatio-temporal atlases 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jano van Hemert
    • 1
  • Richard Baldock
    • 2
  1. 1.National e-Science Institute, University of EdinburghUK
  2. 2.MRC Human Genetics Unit, EdinburghUK

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