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BMC bioinformatics
Feb 19, 2008;9 107.

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

Francisco J Lopez, Armando Blanco, Fernando Garcia, Carlos Cano, Antonio Marin
Author Information
  1. Francisco J Lopez: Department of Computer Science and AI, University of Granada, 18071, Granada, Spain. fjavier@decsai.ugr.es
PMID: 18284669 DOI: 10.1186/1471-2105-9-107

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.

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MeSH Term

Algorithms
Base Sequence
Chromosome Mapping
Databases, Genetic
Fuzzy Logic
Genome, Fungal
Molecular Sequence Data
Pattern Recognition, Automated
Saccharomyces cerevisiae
Journal Article

Word Cloud

Created with Highcharts 10.0.0dataassociationfuzzybiologicalrulesdatabasesknowledgecurrentlydispersedmanyinformationunveilassociationsimpreciseFuzzymodelheterogeneousworkmethodologyyeastgenomecrispcanBACKGROUND:Lastyears'mappingdiversegenomesgeneratedhugeamountsIntegrationavailablevariousrequiredpossiblerelatingalreadyknownBiologicaloftennoisysettheoryspeciallysuitableappropriateintegrateRESULTS:proposenovelbasedruleminingmethodextractionapplydatasetcontainingregardingstructuralfunctionalfeaturesnumberfoundagreeingpreviousresearchareaadditioncomparisonresultsprovesreliableonesCONCLUSION:integrativeapproachonecarriedsignificanthiddenexistingshownintuitivewayusinglinguisticlabelseasy-understandableparametersanalysis:casestudy

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