Mining Association Rule Bases from Integrated Genomic Data and Annotations
Computational Intelligence Methods for Bioinformatics and Biostatistics
An efficient approach for generating frequent patterns without candidate generation
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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GENMINER is a smart adaptation of closed itemsets based association rules extraction to genomic data. It takes advantage of the novel NORDI discretization method and of the CLOSE [27] algorithm to efficiently generate min- imal non-redundant association rules. GENMINER facili- tates the integration of numerous sources of biological in- formation such as gene expressions and annotations, and can tacitly integrate qualitative information on biological conditions (age, sex, etc.). We validated this approach ana- lyzing the microarray datasets used by Eisen et al. [10] with several sources of biological annotations. Extracted asso- ciations revealed significant co-annotated and co-expressed gene patterns, showing important biological relationships between genes and their features. Several of these relation- ships are supported by recent biological literature.