{"title":"Probability Based Most Informative Gene Selection From Microarray Data","authors":"Sunanda Das, A. Das","doi":"10.4018/IJRSDA.2018010101","DOIUrl":null,"url":null,"abstract":"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","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Rough Sets Data Anal.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJRSDA.2018010101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 26
Abstract
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