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Extracting Biological Knowledge by Fuzzy Association Rule Mining

Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Biological data are often heterogeneous, imprecise and noisy. Integration and analysis of this information are required to understand genes roles in cell behaviour. Fuzzy set theory is specially suitable to model imprecise and noisy data and association rules are very appropriate to deal with heterogeneous data. In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method. Interesting relations between functional and structural gene features are obtained. Furthermore, it is shown that fuzzy association rules model these relations in a more intuitive way than previously used techniques.