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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/31183

Title: Fuzzy association rules for biological data analysis: A case study on yeast
Authors: López Domingo, Francisco Javier
Blanco Morón, Armando
García Alcalde, Fernando
Cano Gutiérrez, Carlos
Marín Rodríguez, Antonio
Issue Date: 2008
Abstract: 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. Conclusion An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters.
Sponsorship: This work has been carried out as part of projects TIC-640 of J.A. Seville, and TIN2006-13177 of DGICT, Madrid.
Publisher: Biomed Central
Keywords: Algorithms
Base sequence
Chromosome mapping
Fuzzy logic
Moleuclar sequence data
Saccharomyces cerevisiae
URI: http://hdl.handle.net/10481/31183
ISSN: 1471-2105
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: López, F.J.; et al. Fuzzy association rules for biological data analysis: A case study on yeast. BMC Bioinformatics, 9:107 (2008). [http://hdl.handle.net/10481/31183]
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