Mining spatial gene expression data for association rules (bibtex)
@inproceedings{HB2007,
	_day = {12},
	_month = {03},
	abstract = {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.},
	author = {van Hemert, J.I. and R.A. Baldock},
	booktitle = {Proceedings of the 1st International Conference on BioInformatics Research and Development},
	date-added = {2008-08-18 12:44:11 +0100},
	date-modified = {2016-03-18 11:23:11 +0000},
	editor = {S. Hochreiter and R. Wagner},
	keywords = {data mining; life-science; e-science},
	pages = {66--76},
	pdf = {http://www.vanhemert.co.uk/publications/bird07-Mining_spatial_gene_expression_data_for_association_rules.pdf},
	publisher = {Springer},
	series = {Lecture Notes in Bioinformatics},
	title = {Mining spatial gene expression data for association rules},
	url = {http://dx.doi.org/10.1007/978-3-540-71233-6_6},
	year = {2007},
	bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-540-71233-6_6}}
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