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  -