基于概率的微阵列数据最信息基因选择

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI:10.4018/IJRSDA.2018010101

Sunanda Das, A. Das

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引用次数: 26

摘要

Microarraydatasetshaveawideapplication inbioinformatics研究。Analysis tomeasure theexpressionlevelofthousandsofgenesofthiskindofhigh-throughputdatacanhelpforfindingthe causeandsubsequenttreatmentofanydisease。Therearemanytechniquesingeneanalysistoextract biologicallyrelevantinformationfrominconsistentandambiguousdata。Inthispaper，theconceptsof functionaldependencyandclosureofanattributeofdatabasetechnologyareusedforfindingthemost importantsetofgenesforcancerdetection。Firstly，themethodcomputessimilarityfactorbetween eachpairofgenes。Basedonthesimilarityfactorsasetofgenedependencyisformedfromwhich closuresetisobtained。Subsequently，conditionalprobabilitybasedinterestingnessmeasurementsare usedtodeterminethemostinformativegenefordiseaseclassification。Theproposedmethodisapplied onsomepubliclyavailablecancerousgeneexpressiondataset。Theresultshowstheeffectiveness androbustnessofthealgorithm。关键词:重要基因集，最信息基因选择，概率因子，相似性基因依赖

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Probability Based Most Informative Gene Selection From Microarray Data

Microarraydatasetshaveawideapplication inbioinformatics research.Analysis tomeasure the expressionlevelofthousandsofgenesofthiskindofhigh-throughputdatacanhelpforfindingthe causeandsubsequenttreatmentofanydisease.Therearemanytechniquesingeneanalysistoextract biologicallyrelevantinformationfrominconsistentandambiguousdata.Inthispaper,theconceptsof functionaldependencyandclosureofanattributeofdatabasetechnologyareusedforfindingthemost importantsetofgenesforcancerdetection.Firstly,themethodcomputessimilarityfactorbetween eachpairofgenes.Basedonthesimilarityfactorsasetofgenedependencyisformedfromwhich closuresetisobtained.Subsequently,conditionalprobabilitybasedinterestingnessmeasurementsare usedtodeterminethemostinformativegenefordiseaseclassification.Theproposedmethodisapplied onsomepubliclyavailablecancerousgeneexpressiondataset.Theresultshowstheeffectiveness androbustnessofthealgorithm. KeywoRDS Important Gene Set, Most Informative Gene Selection, Probability Factor, Similarity Based Gene Dependency

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Int. J. Rough Sets Data Anal.

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