Fuzzy association rules for biological data analysis: A case study on yeast

Background: Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data. Results: In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Francisco J. Lopez, Armando Blanco, Fernando Garcia-Alcalde, Carlos Cano, Antonio Marin
• Keywords: Association Rules, Biological Data, BMCBI 2008, Fuzzy Association