<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">van Hemert, J. I.</style></author><author><style face="normal" font="default" size="100%">Baldock, R. A.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">S. Hochreiter</style></author><author><style face="normal" font="default" size="100%">R. Wagner</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Mining spatial gene expression data for association rules</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">biomedical</style></keyword><keyword><style  face="normal" font="default" size="100%">data mining</style></keyword><keyword><style  face="normal" font="default" size="100%">DGEMap</style></keyword><keyword><style  face="normal" font="default" size="100%">e-Science</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-540-71233-6_6</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pages><style face="normal" font="default" size="100%">66--76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><work-type><style face="normal" font="default" size="100%">inproceedings</style></work-type></record></records></xml>