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.


CONCLUSIONS
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.
Lopez
Francisco J
FJ
Department of Computer Science and AI, University of Granada, 18071, Granada, Spain. fjavier@decsai.ugr.es
Blanco
Armando
A
Garcia
Fernando
F
Cano
Carlos
C
Marin
Antonio
A
D000465:Algorithms; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D030541:Databases, Genetic; D017143:Fuzzy Logic; D016681:Genome, Fungal / Q000235:genetics*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics*