Dynamic association rules for gene expression data analysis
نویسندگان
چکیده
منابع مشابه
Mining Spatial Gene Expression Data for Association Rules
We analyse data from the Edinburgh Mouse Atlas GeneExpression 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...
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Over the years, data mining has attracted most of the attention from the research community. The researchers attempt to develop faster, more scalable algorithms to navigate over the ever increasing volumes of spatial gene expression data in search of meaningful patterns. Association rules are a data mining technique that tries to identify intrinsic patterns in spatial gene expression data. It h...
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ژورنال
عنوان ژورنال: BMC Genomics
سال: 2015
ISSN: 1471-2164
DOI: 10.1186/s12864-015-1970-x