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Applied Soft Computing

jo u r n al h om epa g e : w w w . e l s e v i e r . c o m / lo c a t e / a s o c

MicroCBR: A case-based reasoning architecture for the classification of microarray data

Juan F. De Paz

, Javier Bajo

, Vicente Vera

, Juan M. Corchado

aDepartamentoInformáticayAutomática,UniversityofSalamanca,PlazadelaMerceds/n,37008Salamanca,Spain

bDepartamentodeEstomatologiaII,FacultaddeOdontologia,UniversidadComplutensedeMadrid,PlazadeRamonyCajal,s/n,CiudadUniversitaria,28040Madrid,Spain

a rt i c l e i n f o

Articlehistory:

Received13July2009

Receivedinrevisedform25July2011 Accepted14August2011

Availableonline22August2011

Keywords:

Case-basedreasoning HGU133

ESOINN

CLLleukemiaclassification Decisiontree

a b s t r a c t

Microarraytechnologyprovidesagreatamountofdatarequiringanalysis,andtheuseofnewintelligent algorithmsthatidentifytherelevantinformationfortheclassificationprocesshasbecomeessential.This studypresentsaclassificationtoolcalledMicroCBRthatusesthecase-basedreasoningparadigmand incorporatesanovelfilteringtechniquebasedonstatisticalmethods,anewclusteringtechniquethat usesESOINN(EnhancedSelf-OrganizingIncrementalNeuronalNetwork),andaknowledgeextraction techniquebasedontheJ48algorithm.MicroCBRhasbeenappliedtoclassify91CLLpatientsandthe resultsobtainedareshowninthispaper.

©2011ElsevierB.V.Allrightsreserved.

1. Introduction

Theuseofmicroarrays,andmorespecificallyexpressionarrays, enablestheanalysisofdifferentsequencesofoligonucleotides[1,2].

Microarrayscanbeusedinthefieldofmedicinetodetectchanges atthegeneticlevel.Simplyput,amicroarrayisanarrayofprobes thatcontainsgeneticmaterialwithapredeterminedsequence.Pre- determinedsequencescanvaryaccordingtothefieldinwhichthe studyisapplied;inthecaseofhumans,thesequenceswillcontain humangeneticmaterial.Thesesequencesarehybridizedwiththe geneticmaterialofpatients,thusallowingthedetectionofgenetic mutationsthroughtheanalysisofthepresenceorabsenceofcertain sequencesofgeneticmaterial.Thisstudywasconductedtoanalyze theintensitiesproducedduringthehybridization.Theanalysisof thisinformationallowsthedetectionofpatternsthatcharacterize certaindiseasesand,mostimportantly,thegenesassociatedwith thesedifferentdiseases.

Theanalysisofexpressionarraysiscalledexpressionanalysis.

Anexpressionanalysisbasicallyconsistsofthreestages:normal- izationandfiltering;clusteringandclassification;andextraction ofknowledge.Thesestagesarecarriedoutfromtheluminescence valuesfoundintheprobes.Presently,thenumberofprobescon-

∗Correspondingauthor.Address:Compa ˜nía5,37002Salamanca,Spain.

Tel.:+34639771985;fax:+34923277101.

E-mailaddresses:[email protected](J.F.DePaz),[email protected](J.Bajo), [email protected](V.Vera),[email protected](J.M.Corchado).

tainingexpressionarrayshasincreasedconsiderablytotheextent thatithasbecomenecessarytousenewmethodsandtechniques toanalyzetheinformationmoreefficiently[3].Itisnecessaryto developnewtechniquestoanalyzelargevolumesofdata,extract therelevantinformation,anddeletetheinformationwhichhasno relevancetotheclassificationprocess.Moreover,theknowledge obtainedduringtheclassificationprocessisofgreatimportance forsubsequentclassifications.Therearevariousartificialintelli- gencetechniquessuchasartificialneuralnetworks[4,5],Bayesian networks [6], and fuzzy logic [7] which have been applied to microarrayanalysis.Whilethesetechniquescanbeappliedatvari- ousstagesofexpressionanalysis,theirintegrationintosystemsthat combinevariousartificialintelligencetechniques,suchasCBR,is ofparticularinterest.Previousresearch[47]hasstudiedthecapac- ityofdifferentclassifiersinthefieldofsequencing;however,the systemonlyusesnewclassificationsinthelearningphase,andcon- sequentlydoesnottakeintoaccounttherelevanceofthechanges madebyexpertanalysis.ThispaperpresentsMicroCBR,asystem basedonCBR(case-basedreasoning)whichusespastexperiences tosolvenewproblems[8].Assuch,itisperfectlysuitedforsolving theproblemathand.Inaddition,CBRmakesitpossibletoincor- poratethevariousstagesofexpressionanalysisintothereasoning cycleoftheCBR,thusfacilitatingthecreationofstrategiessimi- lartotheprocessesfollowedinmedicallaboratories.Therecovery ofinformationfrompreviousanalysessimplifiestheclassification processbydetectingandeliminatingrelevantandirrelevantprobes detectedinpreviousanalyses.Thisallowsforalearningprocess thatcannotonlyincorporatenewcases,butalsoselecttherelevant informationfromcasesthathavebeenreviewedbyanexpert.

1568-4946/$–seefrontmatter©2011ElsevierB.V.Allrightsreserved.

doi:10.1016/j.asoc.2011.08.021

Forsometimenow,wehavebeenworkingontheidentification oftechniquestoautomatethereasoningcycleofseveralCBRsys- temsasappliedtocomplexdomains[9].TheobjectiveofMicroCBR istodevelopaCBRforclassifyingpatientsbasedoninformation obtainedfrommicroarrays.Thissystemisappliedtotheclassi- ficationof subtypes ofleukemia,specifically,todetectpatterns andextractsubgroupswithintheCLLtypeofleukemia.MicroCBR incorporatesvarioustechniquesofartificialintelligence(filtering, clustering,artificialneuralnetworksandextractionofknowledge) atdifferentstages ofthereasoningcycle.In theretrievephase, newpre-processing andfilteringtechniquesareincorporatedin ordertoselecttheprobeswithrelevantinformationforclassifying patients.Thisinnovativemethodiscapableofobtainingafiltering processthatincorporatesmultiplestatisticaltechniquesbasedon hypothesiscontrasts,whichnotablyreducesthedimensionalityof thedata.Reducingthedimensionalityofthedatamakesitpossible tousetechniqueswithgreatercomputationalcomplexityinlater stagesoftheCBRcycle,whichwouldotherwisebeunviable.The reusestageincorporatesaclassificationtechniquebasedonneural networkESOINN[10].Thistechniqueproposesanovelmethodfor generatingclustersandforidentifyingthenearestclusterforthe finalclassification.ESOINNperforms clusteringbyincorporating boththedistributionprocessoftheentiresurfaceofclassification, andtheseparationbetweengroupswithlowdensityamongthem.

AnadditionalgroupingtechniqueknownasPAM[11](partition aroundmedoids),isalsoused,resultinginamoreaccurateclas- sification,sincetheresultssuggestedbytheESOINNnetworkare comparedtothoseobtainedusingthePAMtechnique.Oncethe classificationprocessisfinished,therevisephaseinitiatesatech- niqueforextractingknowledgeJ48[12]thatcanextractrulesto explainthedecisionstakenintheretrieveandreusestages.More- over,therevisestageincludesaMDS(MultidimensionalScaling) technique[13–15]forpresentinginformationinlowdimension- ality.Ahumanexpertanalyzesthisinformationandevaluatesthe proposedclassificationaswellasthevalidityoftherulesgenerated.

Finally,intheretainstage,ifthehumanexpertconsidersthepro- posedsolutionvalid,thesystemstoresthecaseinformationand therulesthat havebeen obtained.In this way,MicroCBRtakes advantageoftheinformationfrompreviouslyclassifiedpatients, andfromtherelevantandirrelevantprobes,andrulesfromthe previouslyappliedknowledgeextraction.

Thepaperisstructuredasfollows:thenextsectionbrieflyintro- ducestheproblemthatmotivatesthisresearch.Section3presents MicroCBRanddescribesthenovelstrategiesincorporatedinthe stagesoftheCBRcycle.Section4describesacasestudyspecifically developedtoevaluatetheCBRsystempresentedwithinthisstudy, consistingofaclassificationofCLLleukemiapatients.Finally,Sec- tion5presentstheresultsandconclusionsobtainedaftertesting themodel.

2. Microarraydataanalysis

Microarrayhasbecomeanessentialtoolingenomicresearch, making it possible to investigate global gene expression in all aspects of humandisease [16]. Microarray technologyis based onadatabaseofgenefragmentscalledESTs(ExpressedSequence Tags), which are used to measure target abundance using the scannedfluorescenceintensitiesfromtaggedmoleculeshybridized toESTs[17].Specifically,theHGU133plus2.0[3]arechipsused forexpressionanalysis.Thesechipsanalyzetheexpressionlevel of over 47,000 transcripts and variants, including 38,500 well- characterizedhumangenes.Itiscomprisedofmorethan54,000 probesetsand1,300,000distinctoligonucleotidefeatures.TheHG U133plus2.0providesmultiple,independentmeasurementsfor eachtranscript.Theuseofmultipleprobesprovidesacompletedata

setwithaccurate,reliable,reproducibleresultsfromeveryexper- iment. Microarray technology is a critical element for genomic analysisandallowsanin-depthstudyofmolecularcharacteriza- tionofRNAexpression,genomicchanges,epigeneticmodifications orprotein/DNAunions.

Expressionarrays[3]areatypeofmicroarraythathavebeen usedindifferentapproachestoidentifythegenesthatcharacter- izecertaindiseases[7,18,19].Inallcases,thedataanalysisprocess isessentiallycomposedofthreestages:normalizationandfilter- ing,clustering,andclassification.Thefirststepiscriticaltoachieve bothagoodnormalizationofdataandaninitialfilteringtoreduce thedimensionalityofthedatasetwithwhichtowork[20].Since theproblemathandisworkingwithhigh-dimensionalarrays,itis importanttohaveagoodpre-processingtechniquethatcanfacil- itateautomaticdecision-makingaboutthevariablesthatwillbe vitalfortheclassificationprocess.Inlightofthesedecisionsitwill bepossibletoreducetheoriginaldataset.Moreover,thechoice ofaclusteringtechniqueallowsdatatobegroupedaccordingto certainvariablesthatdominatethebehaviourofthegroup.After organizing thedataintogroups it ispossibletoextract knowl- edgeandclassifypatientswithinthegroupthatpresentsthemost similarities.

Case-based reasoning [9] is particularly applicable to this problemdomainbecauseit(i)supportsarichandevolvablerep- resentationofexperiences,problems,solutionsandfeedback;(ii) providesefficientandflexiblewaystoretrievetheseexperiences;

and(iii)appliesanalogicalreasoningtosolvenewproblems[21].

CBRsystems canbeusedtoproposenewsolutions orevaluate solutions toavoidpotentialproblems.Theresearchin[22]sug- gests that analogical reasoning is particularlyapplicable tothe biological domain, in partbecausebiological systems are often homologous(rootedinevolution).Moreover,biologistsoftenusea formofreasoningsimilartoCBR,whereexperimentsaredesigned andperformedbasedonthesimilaritybetweenfeaturesofanew systemandthoseofknownsystems.In[23]amixtureofexperts forcase-basedreasoning(MOE4CBR)isproposed.Itisamethod thatcombinesanensembleofCBRclassifierswithspectralclus- teringandlogisticregression,butdoesnotincorporateextraction of knowledgetechniquesand doesnot focusondimensionality reduction.Someapproachessuchas[9]provideCBRsolutionsand knowledgeextractiontechniques,facilitatingthecomprehension oftheclassificationprocess.ThispaperpresentsMicroCBR,aCBR solutionwhichalsoincorporatesnewknowledgeextractiontech- niques, but additionallyfocuses onthe definitionof innovative strategiesfordimensionalityreductionandclustering.Thefollow- ingsectionpresentsadetailedaccountoftheCBRsystemproposed inthiswork.

3. CBRsystemforclassifyingmicroarraydata

ThissectionpresentstheCBRsystemproposedinthecontext ofthisresearchandprovidesaclassificationtechniquebasedon previousexperiencesfordatafrommicroarrays.TheCBRdeveloped systemimitatesthebehaviourofhumanexpertsinthelaboratory andincorporatesinnovativeknowledgediscoverytechniques.The systemreceivesdatafromtheanalysisofchipsandisresponsible forclassifyingindividualsbasedonevidenceandexistingdata.

ThepurposeofCBRistosolvenewproblemsbyadaptingsolu- tionsthathavebeenusedtosolvesimilarproblemsinthepast[8].

TheprimaryconceptwhenworkingwithCBRsistheconceptof case.Acasecanbedefinedasapastexperience,andiscomposedof threeelements:aproblemdescriptionwhichdescribestheinitial problem;asolutionwhichprovidesthesequenceofactionscar- riedoutinordertosolvetheproblem;andthefinalstagewhich describesthestateachievedoncethesolutionwasapplied.CBR

managescases(pastexperiences)tosolvenewproblems.Theway casesaremanagedisknownastheCBRcycle,andconsistsoffour sequentialstepswhicharerecalledeverytimeaproblemneedsto besolved:retrieve,reuse,reviseandretain.EachstepoftheCBRlife cyclerequiresamodelormethodinordertoperformitsmission.

IntheCBRsystemproposedwithinthisstudy,theretrievephase filtersvariables,andrecoversimportantvariablesfromthecasesto determinethemostinfluentialfortheclassification.Oncethemost importantvariableshavebeenretrieved,thereusephasebegins adaptingthesolutionsfortheretrievedcasestoobtainthecluster- ing.Oncethisgroupingisaccomplished,thenextstepisknowledge extraction.Therevisephaseconsistsofanexpertrevisionforthe proposedsolution,andfinally,theretainphaseallowsthesystemto learnfromtheexperiencesobtainedinthethreepreviousphases, consequentlyupdatingthecasesmemory.

AkeyelementinaCBRsystemisacase,whichcanbedefined asapastexperience[8]andiscomposedofthreeelements:prob- lemdescription,problemsolution,andthefinalstateobtainedafter applyingthesolution.AcaseinMicroCBR contains information relatedtothepatient,therules,theproposedclassification,and theprobesmarkedasirrelevantorimportant.Thecaseisdefined bythefollowingexpression:

i_j=(id,S=(A₁,...,A_n),C^p,C^r)

wherei_j∈IandI={i₁,...,i_S}isthesetofindividuals/cases,Aisthe setofalltheprobes,A_irepresentstheprobei,C^pisthepredicted classandC^rtheactualclass.

In addition tothecases memory, oursystemincorporates a memoryofrulesthatcontainstheinformationextractedthrough theknowledgeextractiontechniques.Thememoryofrulesisstruc- turedasfollows:

R={r1,...,rl}, with r_i=(l1∧...∧lm)→c_j

where ls=(dts,os,)/dts∈Dt,os∈O,S_Irr⊆A whereRisthesetofrulesfromthedecisiontree,l_scontainsasetof discretizedprobes,anoperatorandarealvalue,Dtisthediscretiza- tionvaluefortheprobeAt,O={=, =/,>,<,≤,≥}operator,andSIrr

isthesetofprobesmarkedasirrelevant,cj∈C^p.

Whenanewcaseisclassified,anewdecisiontreeisgenerated intherevisestage.Asetofrulesareextractedfromthedecision treewhichprovideknowledgeabouttherelevanceoftheprobes intheclusteringandclassificationprocess.Fig.1showsascheme ofthetechniquesappliedinthedifferentstagesoftheCBRcycle.

AsseeninFig.1,theimportantprobesthatallowtheclassifica- tionofpatientsarerecoveredintheretrievephase.Theretrieve phaseisdividedinto6sub-phases:pre-processingthroughRMA, removalofirrelevantvariables,uniformdistribution,probeswith- outmeaningfulcut-offpoints,andcorrelatedvariables.Inthereuse phasethepatientsaregroupedbymeansofanESOINNneuralnet- work.Then,thepatientswithnopriorclassificationareassigned toagroupusingthenearestcluster.IntherevisephasetheJ48 [12]techniqueisappliedforextractingknowledgeaboutthemost importantprobes forthe classification,and theMDS technique [13–15]isusedtomakearepresentationinlowdimensionality.

Finally,intheretainphase,theknowledgeisupdated.Thisknowl- edgeincludesthecaseclassification,thedecisionrulesobtained, andtheinformationassociatedwiththeimportanceorirrelevance ofcertainprobesextractedfromtherules.

Fig.1showstheschemeoftheCBRsystemproposedwithinthis study.Fig.2detailsthestepsfollowedineachofthestagesofthe CBRcycle.ThestructureoftheMicroCBRwillnowbeexplainedin detail,presentinginnovativetechniquesmodelledineachofthe stagesoftheCBR.

Fig.1.StructureoftheproposedMicroCBRmodel.

3.1. Retrieve

Traditionally,onlythecasessimilartothecurrentproblemare recovered,oftenbecauseofperformance,andthenadapted.With regardtoexpressionarray,thenumber ofcasesisnotacritical factor,rather thenumberofvariables. Forthis reason,wehave incorporatedaninnovativestrategywherevariablesareretrieved atthisstageandthen,dependingontheidentifiedvariables,therest ofthestagesoftheCBRarecarriedout.Thenewstrategyallowsa notablereductioninthedimensionalityofthedata.Thefiltering phaseiscarriedoutonItogetherwiththenewcaseis.Thefiltering isonlyappliedtothoseprobesnotassociatedwithanyoftherules.

First,apre-processingofthedataisconductedusingRMA.Then, the5filteringsub-phasesareexecuted:removalofcontrolprobes, removaloferroneousprobes,removaloflow variabilityprobes, removalofprobeswithauniformdistribution,andremovalofcor- relatedprobes.Thesefivesub-phasesareoutlinedinthefollowing paragraphs.

3.1.1. RMA

Thisphasebeginsoncethelaboratoryexperimentwithmicroar- rayshasbeencompleted.Theresearcherobtainsvariousfilesthat contain grossintensityvalues. Prior toanalyzing thedata, itis important to complete the pre-processing phase, which elimi- nates defective samples and standardizes the data. As seen in Fig. 2, this phaseis normally divided into 3 sub-phases:back- groundcorrection,standardization,andsummarization.Thereis currently a limited group of algorithms that investigators use for performingthesesteps.ThemostcommonareMAS5.0[24]

(MicroarrayAffymetrixSuite5.0),PLIER[25](ProbeLogarithmic IntensityError),andRMA)[26](RobustMulti-arrayAverage).

The RMA [26] algorithm is a method for normalizing and summarizingprobe-levelintensitymeasurements.Itanalyzesthe valuesforthePM(Perfect-Match):inthefirststep,aBackground Correctioniscarriedouttoremovethenoisefromtheaveragesof thePM;inthesecondstep,thedataisquantilenormalizedinorder tocomparedatafromdifferentmicroarrays;finally,asummariza- tionismadeandthevaluesforeachprobe-setaregenerated.

Fig.2. DetailedarchitecturefortheMicroCBRmodel.

3.1.2. Irrelevantprobes

Oncethecontrol andtheerroneous probeshave beenelim- inated, the filtering process begins. The first step consists of eliminatingtheprobesmarkedasirrelevantinpreviousexecutions oftheCBRcycle.Thisway,allprobesthatcanpassthefiltering phase,butarepronetocauseerroneousresultsduringthereuse phase,areremoved.

3.1.3. Variability

Thesecondstageistoremovetheprobesthathavelowvariabil- ity.Thisworkiscarriedoutaccordingtothefollowingsteps:

1.Calculatethestandarddeviationforeachoftheprobesj

_·j=+

n

j=1

(¯_·j−x_ij)² (1)

wherenisthetotalnumberofcases,¯_·jistheaveragepopula- tionforthevariablej,andx_ijisthevalueoftheprobejforthe individuali.

2.Standardizetheabovevalues z_i= _·j−

(2)

where =(1/n)

j=1_·j and _·j=+

(1/n)

j=1(¯_·j−x_ij)² wherez_i≡N(0,1)

3.Discardprobesforwhichthevalueofzmeetsthefollowingcon- dition:z<−1.0.Thiswillachievetheremovalofabout16%ofthe probesifthevariablefollowsanormaldistribution.

3.1.4. Uniformdistribution

Finally,allremainingvariablesthatfollowauniformdistribu- tionareeliminated.Thevariablesthatfollowauniformdistribution willnotallowtheseparationofindividuals.Therefore,thevariables thatdonotfollowthisdistributionwillbereallyusefulvariables in theclassificationof thecases.Thecontrastofassumptionsis explainedbelow,usingtheKolmogorov–Smirnov[27]testasan example.H0:thedatafollowauniformdistribution;H1:theana- lyzeddatadonotfollowauniformdistribution.Statisticalcontrast:

D=max{D⁺,D⁻} (3)

where D⁺=max

1≤i≤n{(i/n)−F₀(x_i)}, D⁻=max

1≤i≤n{F₀(x_i)−((i−1)/n)} withiasthepatternofentry,nthenumberofitemsandF₀(x_i)the probabilityofobservingvalueslessthaniwithH₀beingtrue.The valueofstatisticalcontrastiscomparedtothenextvalue:

D˛= C˛

k(n) (4)

Inthespecialcaseofuniformdistributionk(n)=√

n+0.12+ (0.11/√

n)andalevelofsignificance˛=0.05,C˛=1.358.

3.1.5. Cut-offpoints

Thisstepremovestheprobesthat,despitenotfollowingauni- formdistribution,havenoseparationbetweenelements,anddo notallowtheelementstobepartitioned.Thewaytoremovethe probesistodetectchangesinthedensitiesofthedata,andtoselect thefinalprobes.Theprobesinwhichcut-offsorhighdensitiesare notdetectedareeliminated,astheydonotprovideusefulinfor- mationtotheclassificationprocess.Thiswillkeeptheprobesthat allowtheseparationofindividuals.Thedetectionoftheseparation intervalsisperformedbycalculatingthedistancebetweenadjacent

Select a probe

Sort the values

Remove elemets

< threshold Uniform distribution

Confidence

interval Percentil

Cluster Cluster

More probes

yes no

No

yes 1, 2

3, 4

5

6, 7

8 10

10

11

12

Fig.3.Cut-offpointsoverview.

individuals.Oncethedistanceiscalculated,itispossibletodeter- minethepotentiallyrelevantvalues.Theselectioniscarriedoutby applyingconfidenceintervalsforthevaluesofthesedifferencesif thevaluesfollowauniformdistribution,orbyselectingthevalues aboveacertainpercentileifthevaluesdonotfollowanormaldis- tribution.ThediagraminFig.3illustratesthedetectionprocessfor cut-offpoints.

Thisprocessisformalizedasfollows:

1.LetIbethesetofindividualswithfilteredprobestogetherwith thenewindividual,wherex_·jrepresentstheprobejforallthe individuals,andx_ijtheindividualifortheprobej

2.Selecttheprobej=1,x_·j

3.Sortinincreasingordervaluesx_·j

4.Calculatethevalueforx_ij=x_i+1j−x_ij

5.Determineifthevariablex_ijfollowsauniformdistributionby meansoftheShapiro-Wilktest[28],otherwisegotostep10.

6.Calculatethevaluefor ¯x_.j

7.Establish the confidence interval for the variance, which is established as ²_·j∈[((n−1)·S²)/(²_n

−1,1−˛/2),((n−1)· S²)/²_n

−1,˛/2] with ˛=0.05 and n=#x_·j and the number of elementsforx_·j,S²isthesamplingvariance.

8.Establishthesetofelementsformx_ijnotbelongingtotheset Q_j={x_ij/x_ij >I⁺

·j}whereI⁺

·j

isthevalueoftheupperlimitof theconfidenceinterval.

9.Gotostep11.

10.SelectthosevaluesuptothepercentileP_˛fromeveryx_·jand establishthesetQ_j={x_ij/x_ij>P˛}

11. Selecttheprobej+1inthecaseofmoreprobesneedingrevision andgotostep2.

12.CreatethenewsetofprobesI=∪x_·j/∃^x_ij∈Q_j/i>#x·u∧i<

#x−#x·u.Whereuisaconstantbetween[0,0.5].

13.Finalizeandreturnthenewsetofindividualswiththefiltered probesI.

3.1.6. Correlations

Atthelaststageofthefilteringprocess,correlatedvariablesare eliminatedsothatonlytheindependentvariablesremain.Tothis end,thelinearcorrelationindexofPearsoniscalculatedandthe probesmeetingthefollowingconditionareeliminated.

rxiy_·j<˛ (5)

given ˛=0.95, rx_·iy_·j=(x_·ix_.j/x_·ix_·j), _x·ix·j=(1/N)

3.2. Reuse

Inthisphasetheclusteringofindividualsiscarriedout,along withtheclassificationofnewindividualstooneoftheclusters.

Thereareseveralalgorithmsforclustering,butthemostcommon arethehierarchicalalgorithms[29]andthosebasedonpartition- ing[11].Withinthehierarchicalalgorithmsthemostcommonis the dendrogram[29]. Among thepartition-basedmethods it is possibletofindalternativesbasedonRNAssuchasSOM[30](Self- OrganizingMap),GNG[31](GrowingNeuralGas)orSOINN[32]

(Self-Organizing IncrementalNeuronalNetwork).Otheralterna- tivesincludek-means[33]orPAM[11].Thesetwomethodsdefine aseriesofinitialclusterswhichcorrespondtoanewindividualor toexistingindividuals,andaremarkedastheclusterrepresenta- tives,whiletheremainingindividualsareallocatedtothenearest cluster.Theproblemwitheachofthesemethodsisthattheydonot considerchangesinthedistributionofdensitiesofindividuals,and usuallydonotdetectclusterswithatypicalforms,suchaselongated clusters.Analternativetoclustermethodscouldbethedirectappli- cationofclassifiers,includingoptimization-basedfunctionssuchas SVM[48],decisiontrees,decisionrules,ordiffusemethodssuchas thek-nearestneighbour.

3.2.1. ESOINNneuralnetwork

NeuralNetworksbasedonGNGcandetectclusterswithatyp- icalforms,adjustiterativelytothedistributionoftheindividuals, anddetectlowdensityzones.TherearevariantsoftheGNG,such as the GCS [34] (Growing Cell Structure) or SOINN [32] (Self- OrganizingIncrementalNeuronalNetwork).Unlikeself-organizing mapsbasedonmeshes,GrowingGridorGCSdonotsetthenum- berofneurons,orthedegreeofconnectivity,buttheydoestablish thedimensionalityofeachmesh.Thiscomplicatestheseparation phasebetweengroups oncetheneurons aredistributed evenly acrossthe surface.The ESOINNneuralnetwork [10] (Enhanced Self-OrganizingIncrementalNeuronalNetwork)isavariation of theSOINNneuralnetwork [32],which allowsthecreation ofa singlelayer,whileESOINNisabletoincorporateboththedistri- butionprocessalongthesurfaceandtheseparationbetweenlow densitygroups.Thelearningprocessofthenetworkisdistributed intotwostages:thefirststageofcompetitionCHL[34](Competi- tiveHebbianLearning)wheretheclosestnodetotheinputpattern isselected;andthesecondadaptation/growingstagesimilartoa GNG.Thetrainingphaseandthevariousalgorithmsappliedatevery modifiedstageareoutlinedbelow:

1.Updatetheweightsofneuronsbyfollowingaprocesssimilarto theSOINN,butintroducinganewdefinitionforthelearningrate inordertoprovidegreaterstabilityforthemodel.Thislearning

ratehasproducedgoodresultsinothernetworkssuchasSOM [36].

W_a1=n₁(M_a1)(−W_a1)

Wa_i=n₂(Ma₁)(−Wa_i) witha_i∈Na₁ (6) wheren1(x)=(1/√

x),n2(x)=(1/

2+x²),a_iisneuroni,is theinputpattern,Ma1isthenumberofwinningsofneuronai, Na₁isthesetofneighboursofa_i.

2.Deletetheconnectionswithhigherage.Theagesarestandard- izedandthosewhosevaluesareintheregionofrejectionwith k>0areremoved.Theassignedvalueof˛is0.05,therefore z_i= e_i−

,z≡N(0,1) thenf(z)= 1

√2exp −z² 2

(7) where P(z<k)=˛/2→P(z<k)=0.975→(z)=0.975, k=1.96.

Thereforeallzvaluesthataregreaterthan1.96aredeleted.

3.OnceallinputpatternshavebeenintroducedthenaKS-Test[27]

iscarriedoutinordertodetermineifthedensitydistributionfor theneuronsineachgroupfollowsanormaldistribution.Ifso, thelearningprocedureisfinished;otherwisethenextpatternis processed.Thevalueof˛chosenis0.05.

Oncethecaseshavebeendistributedinthemeshes,itisnec- essarytoassigneach ofthemeshestoa class accordingtothe followingprocedure:LetIbethesetofindividualsoncetheprobes havebeen filteredand G^E theset ofclusterscreatedby means oftheESOINNneuralnetwork,definedasG^E={g^E/g^E⊆I},where g_i^E∩g^E_j = ,∀ⁱ=/j with g_i^E,g_j^E∈G^E. Let C be the set of existing classesfortheindividualswherec_j∈Cistheclassjintheset.Wecan saythatthemeshi,g^E_i belongstoaclassj,g^E_i∈c_jandisrepresented asg_i^E

cjwhen

max_cj∈C#Ic_j/g_i^E

#Ic_j ·#I_cj

#I (8)

whereI_cj ={s∈I/s∈c_j}andI_cj/g^E_i isthesetofindividualsfromc_j restrictedtothegroupg_i^E.

ThesetofmeshesbelongingtotheclassisdenotedasG^E_cjandis definedbytheexpression(9).

G^E_cj=

g^E icj∈G^E

g^E_i

cj (9)

3.2.2. PAM

ThePAMalgorithm[11]isexecutedinparalleltotheclustering inordertofacilitateacomparisonoftheresultsobtained.Theclas- sificationmadebybothmethods,PAMandESOINN,generatesan equivalenceindexbetweenthetwomethodsthatdeterminesthe consistencyofthereusephase.ThealgorithmusedforPAMisas follows:

1.Selectthenumberofclustersdependingon#C.

2. Themetricusedforthedistanceisthesameastheoneusedin theESOINNnetwork

3.Classifythepatientstakingallofthevariablesintoaccount,with- outanyfilteringG^P={g^P/g^P⊆I}withg^P_i ∩g_j^P= ,∀ⁱ=/j 4. OncethegroupsG^Parecreated,anassignationismadefollowing

theprocedureindicatedin(8).

3.2.3. Equivalenceindex

OncetheindividualshavebeenclassifiedusingboththePAM andtheESOINNneuralnetworks,theequivalenceindexforboth

Table1 Numberofprobes.

Uniform Cut-off Correlations

CBR 1311 618 541

methodseqiscalculated,andtheerrorratefortheESOINNnet- workisdeterminedasafunctionofthepre-classifiedcases.The equivalenceindexisdefinedasindicatedin(10):

eq= #{i∈I/i∈c^E_j ∧i∈c^P_j}

#I (10)

wherec_j^Erepresentsthesetofmeshesbelongingtoclassjthrough the ESOINN network and c_j^P represents the set of individuals belongingtoclassjthroughthePAMalgorithm.

3.2.4. Classification

Oncethemeshesaregeneratedbytheclusteringprocess,pre- viously unclassified individuals are now classified by selecting thenearestmesh.Whenthemeshhasbeenselected,thecaseis assignedtotheclassofthemeshselected.Theassignmentisdefined as(11).

is∈G^E_k

cj→iu∈C_j (11)

whereiuistheunclassifiedindividual,isistheindividualclosestto theindividualandiucalculatedusingtheEuclideandistance.

Fig.2describesthefunctioningofthereusephase.Asshown inFig.2,thereusestagereceivesthefilteredandnot-filtereddata resultingfromtheretrievestageasinputs.Theinputisusedforboth theESOINNneuralnetworkandthePAMtechnique.TheESOINN neural network generates a setof groups assigned todifferent classes.Thesegroupsarecomposedofmeshescontainingdifferent elementstogetherwiththeinformationofthepreviousclassifica- tion.ThePAMtechniquerepeatsthesameprocessconcurrentlyand generatesthegroupsforeachoftheclasses.Thegroupsgenerated byPAMcontaintheindividualsandtheirpreviousclassification, butdonotconsidersub-groups.Finally,theequivalenceindexis calculatedandthenewpatientisclassified.Theerrorrateforthe ESOINNnetworkismadethrough(8) todeterminetheclassfor eachofthegroups.

3.3. Revise

AsshowninFig.1,therevisioniscarriedoutbyahumanexpert whodeterminesifthegroupassignedbythesystemiscorrect.To facilitatethehumanexperttask,theequivalenceindexandthe errorratewerecalculatedinthereusestage.It isimportantfor themedicalhumanexperttounderstandtheclassificationprocess performedinthetwopreviousstages.Forthisreason,MicroCBR providesaknowledgeextractionmethodintheRevisephase.This methodanalysesthestepsfollowedintheretrieveandreusestages, and extractsknowledge whichisthenformalizedin rules.Con- sequentlythehumanexpertcaneasilyevaluatetheclassification andextractconclusionsconcerningtheefficiencyoftheclassifica- tionprocess.Theknowledgeextractionphasedetectsanomalous classifications,sinceitaccountsfortheexistenceofprobeswith irrelevantinformation,orthosethatweredecisiveinthemisclassi- fication.Forexample,thereareprobeswhichseparateindividuals aseithermenorwomen.Similarly,thehumanexpertcanmark importantprobesthatarenoteliminatedintheretrievestagedur- ingthefiltering.

Theextractionofknowledgethatis presentedtothehuman expertiscarriedoutusingtheJ48algorithm[12].TheJ48algo- rithmistheJavaimplementationoftheC4.5algorithm,anevolution

Fig.4. Densityplotandhistogramforprobes1552280 atand219703atthatdonotmeettheuniformitytestandinwhichcut-offpointsweredetected.

Table2

Classificationerrors.

Type1 Type2 Type3

Total 46 12 33

Predicted/Erroneous 47/5 9/0 35/4

oftheoriginalID3[37],whosemainadvantageisthatitincorpo- ratesnumericalattributesintothelogicaloperationscarriedoutin thetestnodes.Thereareotheralternativesforgeneratingdecision ruleswhichoperatesimilartothedecisiontrees,includingRIPPER [38]andPART[39].Thesemethodsextractsimilarinformationto classifyindividualsaccordingtodecisionrules.Whiletheresults aresimilar,thetreesallowtheexperttoapplyamoreintuitive classificationofthecases.BeforeapplyingJ48,itisnecessaryto performadiscretizationofthevaluessothatthealgorithmiscapa- bleofworkingwithlargevolumesofinformation.Thereasonfor thisisthesearchforthecut-offpointsinthevariablesusesastrat- egythatdependsmainlyonthenumberofdifferentvaluesthata variablecontains.

Table3

Comparisonofmethods.*,differentmedian;=,equal;(−),medianofcolumnless thanmedianofrow.

CBR Dendrogram PAM SVM KNN

CBR

Dendrogram *(−)

PAM *(−) *(−)

SVM =(−) *(+) *(+)

KNN *(−) *(+) *(+) *(−)

Thegeneralobjectiveofextractionofknowledgetechniquesis toprovideahumanexpertwithinformationaboutthesystem- generatedclassificationbymeansof asetof rules thatsupport thedecision-makingprocess.Itshouldbenotedthatknowledge extractiontechniquesarenotintendedtosubstitutetherationale andexperienceofa humanexpert duringadiagnosis,ratherto complementtheprocessandserveasanadditionalmethodology orguidelineforcommonproceduresinanalysis.

Theprocessisdescribedinthefollowingsteps.LetIbetheset ofindividualsandsthenumberofprobesoncethefilteringprocess hasfinished:

fr: A1×...×As→A₁×...×A_s

(a₁,...,as)→fr(a₁,...,as)=(a₁,...,a_s)

whereA_i∈[0,1]isthevalueofthetermiusingthefunctionfrand isobtainedinthefollowingway:

a_i= a_i−min(A_i) max(A_i)−min(A_i)

Finallythevaluesarediscretizedbymeansoffuinaseriesof predefinedlevelst.

fu: A₁×...×A_s→

D×...×D (a₁,...,a_s)→fu(a₁,...,a_s)=(d₁,...,d_s) whereD=

i∈{0,...,t−1}i·(1/(t−1)),wecansaythatd_i=d_jifapply- ingthefunctionfu,withd_j∈Difd_i∈[d_j−1/(2t), d_j+1/(2t)].

Oncethetransformationis finished,thesetof individualsis determinedbythesubsetI⊆

D×...×Dofthedata,andJ48[12]

Fig.5. Decisiontree.

isusedtogeneratetherulesthatclassifytheindividuals.Theuse ofJ48,allowsrulestobeobtainedforclassifyinganindividuali_k∈I totheclassc_jbymeansofrulessimilarto:

r_i=(l₁∧...∧lm)→c_j

wheredpisthevaluefortheattributepfortheindividuali_k.Inthis waythesetisdefinedforrulesRthatclassifytheindividualsfor eachoftheclasses.Theserulescanberepresentedasadecision tree.

Fig.2representstherevisestageforMicroCBR.Theinputcorre- spondstothediscretizationofthevalues(ifthereusephasehas beensuccessful). Subsequently,knowledgeextractionisapplied through the J48. Finally, the relevant information extracted is stored(inconsequentialprobes,importantprobes,andresultsof the classification). At this stage, in addition to therepresenta- tionofthedecisiontree,a3Drepresentationwiththeinformation retrievedisdisplayed.ThedimensionalityisreducedbyusingMDS [13–15]

3.4. Retain

Ifthehumanexpertidentifiesrelevantinformationattherevise stage,theknowledgeisacquiredandtheinformationobtainedis stored.Theinformationthatisstoredcorrespondstotheclassifi- cationsthatareconsideredcorrect,thedecisionrulesgenerated thatareconsideredrelevant,andtheprobesmarkedasirrelevant.

Fig.6.Illustrationscomparingprobesfoundintherulesfortheknowledgeextractionstage.Withinthediagonal,theillustrationrepresentsthedensitiesforeachprobe.For theillustrationsoneithersideofthediagonal,thegraphisobtainedusingtheprobescorrespondingtoitsrowandcolumn+class3individuals,class2,andoclass1.

Fig.7.MainprobesrecoveredusingJ48.

TheinformationstoredisdividedintothecasesmemoryIandthe memoryofrulesRandS_Irr.

Fig.2showsthestructureoftheretainstage.Takingtherevision oftheexpertintoaccount,thesystemlearnsfromthenewexpe- rienceandstorestheinformationthattheexpertestablishedas relevant.Thestoredinformationcanincludeprobes,classifications andrules.

4. Casestudy:classificationofCLLleukemiapatients

Microarray analysishasmade it possibletocharacterizethe molecularmechanismsthat cause severalcancers. With regard to leukemia, microarray analysis has facilitated the identifica- tion of certain characteristic genes in the different variants of leukemia[7,35,40].Cancerexpertsremarkontheimportanceofthe

identificationofthegenesassociatedtoeachtypeofcancerinorder toestablishthemostefficienttreatmentsforthepatients[41,42].

TheCancerInstituteinthecityofSalamancawasinterestedinnovel toolsfordecisionsupportintheprocessofCLL(ChronicLympho- cyticLeukemia)patientclassification.

TheInstituteprovideduswith91samplesofpatientdataand askedforatooltoprovidedecisionsupportintheexpressionarray analysisprocessandtoincorporateinnovativetechniquestoreduce thedimensionalityofthedataandidentifythevariables witha higherinfluenceintheclassificationofpatients.Thesamplescorre- spondedtopatientsaffectedbychroniclymphocyticleukemia.CLL isadiseaseoflymphocytesthatappeartobematurebutarebiolog- icallyimmature.TheseBlymphocytesarisefromasubsetofCD5-B cellsthatappeartohavearoleinautoimmunity.Thepathogene- sisofchroniclymphocyticleukemiaislikelyamultistepprocess,

Table4

Classifiedcasesforeachtypedetectedaccordingtothetotalnumberofexisting elements/classifiedelements/erroneouselements.

Type1 Type2 Type3 Total

46/48/5 12/9/0 33/34/3 91

42/44/5 10/8/0 29/29/3 81

37/38/4 8/8/1 26/25/2 71

31/30/3 7/8/3 23/23/2 61

26/24/2 5/7/3 20/19/1 51

24/24/3 5/4/0 17/18/3 46

initiallyinvolvingapolyclonalexpansionofCD5-Bcellsfollowedby thetransformationofasinglecell[43].CLLisoneofthefourmain typesofleukemia.About15.110newcasesofCLLwillbediag- nosedin2008.Approximately90.179peoplearecurrentlyliving withCLL,morethanthenumberofpeoplelivingwithanyother typeofleukemia.MostpeoplewithCLLareatleast50yearsold [44].CLLstartswithachangetoasinglecellcalledalymphocyte.

Overtime,theCLLcellsmultiplyandreplacenormallymphocytes inthemarrowandlymphnodes.ThehighnumberofCLLcellsin themarrowmaycrowdoutnormalblood-formingcells,andCLL cellsarenot abletofightoffinfectionlikenormal lymphocytes do[44].Theaimofthetestsperformedinthisstudyistodeter- minewhetheroursystemisabletoclassifynewpatientsbasedon previouslyanalyzedandstoredcases.

5. Resultsobtained

Thispaper has presented MicroCBR, a case-based reasoning systemspecifically designed to analyze data from microarrays, facilitatingthegroupingandclassificationofindividuals.Moreover, MicroCBRprovidesaninnovativemethodforexploringtheclassifi- cationprocessandextractingknowledgeintheformofruleswhich helpthehumanexpertstounderstandtheclassificationprocess andobtainconclusionsabouttherelevanceoftheprobes.MicroCBR wasappliedtoa realcasescenarioforclassifyingCLLleukemia patientsattheCancerInstituteinthecityofSalamanca.Thehuman expertsinthelaboratoryhaveremarkedontheadvantagesofusing MicroCBRasadecisionsupportsystemforCLLclassification,and haveespeciallynotedthefacilityinacquiringknowledgeandexpla- nations.

Section4presentedthecasestudyconsideredinthis report, whichclassified91CLLleukemiapatientsintogroups.Theaimof thecasestudywastoidentifytheprobesthatallowclassifyingthe CLLleukemiapatientsintosubgroups.Inaninitialtest,datafrom 90patients,wherethepreviousclassificationwasnottakeninto account,wereusedintheMicroCBRsystem.Thepre-processing phasebeganwith54.675probesandtheRMAwasappliedtoobtain theluminescencevaluesforeachoftheprobesandtohomogenize thevalues fromdifferentchips.Afterthepre-processing phase, thefilteringprocesswasapplied,notablyreducingtheprobesto 618,withoutincreasingtheerrorrate.Thecut-offphasereduced theprobescomingfromtheuniformfilteringfrom1.311to618 probes.Table1showsthenumberofprobesgeneratedaftereach ofthefilteringstages.Fig.4a–dpresentstwoprobesthatpassed theuniformitytestandcontaincut-offpoints.Fig.4aandcshowsa densityplotandFig.4banddpresentthehistogramfortheprobes 1552280atand219703at.AsseeninFig.4,neitheroftheprobes followsauniformdistribution,andbothofthemhavelowdensity pointswhichallowaseparationofindividuals.Thisstagetakesthe irrelevantprobesfilteringintoaccount,asprobe201909at,which isassociatedtotheYchromosome.

Thereusephasebeginswhentheprobeshavebeenfiltered,and generatesthemeshesforthegroupsaswellasthedistributionof theindividualsalongthespace.Themeshclosesttothenewcase wasthenselectedandtheclassificationwasmade.Toevaluatethe

Fig.8. RepresentationoflowdimensionalityprobeswithMDS.

proposedmodel,thesystemclassified90individualsandonenew individual,andtheresultsobtainedwerecomparedtotheprevi- ousexistingclassifications.Table2showsthenumberofelements withanerroneousclassification.Thefinalclassificationwascom- paredwiththedataobtainedusingaclusteranalysisdendrogram [45],PAM[11],theSVMclassificationtechnique[48]andKNN[49]

onthefiltereddata,andtheresultsofthefinalclassificationwere showntohavealowerrateoferror.Theproportionoferrorsin everygroupwascalculatedandtheKurskal–Wallistest[46]was applied,asshowninTable3,todeterminateifthemedianofthese proportionswasequal.Thistestwasperformedbyapplyinga5×2 crossvalidationfollowedbytheKurskal–Wallistest.Weoptedfor theKurskal–Wallistestbecauseitisanon-parametrictechnique thatdoesnotrequireanysuppositionsaboutthedata;asanalter- nativetheMann–Whitneycouldhavebeenused.Thedifference obtainedbetweenCBRandSVM wasnot consideredsignificant, althoughthemedianwaslower.

Intherevisephase,MicroCBRextractedtheknowledgeobtained duringtheclassificationprocess,asshowninFig.5.Fig.5presents atreewherethenodesareprobesandthebranchesaredecision rules.Theleafnodescontainthegroupassignedandthenumberof elementsclassifiedcorrectlyandincorrectly.Thetreerepresented inFig.5istheresultoftheclassificationofthe90patientsand thesinglenewpatient.Fig.6representssomegraphicswherethe valuesoftheretrievedprobesarecomparedtwobytwowithJ48 [12],andthediagonalshowsthedensitygraph.AsseeninFig.6,

thereareindividualsbelongingtoclass3whichcanbeeasilysep- aratedformtherest.Fig.7a–dshowsthedatacorrespondingto thefirstthreeprobesrecoveredwithJ48.Fig.7aandbshowsthe valuesfortheprobesoftheindividuals.Theindividualsforeach ofthegroupsarerepresentedindifferentcolours,andaregres- sionplaneisobtainedforeachofthegroups.Thegraphshowshow theindividualscanbeseparatedinthespaceusingtheseprobes.

Fig.7canddshowsthesameinformationbutellipsoidshavebeen addedinordertogroup50%ofthedataforeachofthegroups,thus facilitatingthespatialseparationofthedata.Toobtainavisualrep- resentationofthepatient’sclassification,weusetheMDS[13–15], andthedimensionalityofthedataisreducedtothree.Fig.8aand brepresentstheinformationonceMDShasbeenappliedand,as shown,theindividualsofthedifferentclustersareseparatedinthe space.

Aftercarryingoutexperimentalstudiesofthedifferentproce- duresappliedtothereasoningcycle,thesystemperformancewas evaluated.Inordertoevaluatetheglobalfunctioningofthesys- tem,weincludedanadditionaltest.InthistestMicroCBRcontains 45previouslyclassifiedindividuals,andaimstoclassifytheremain- ing46casesusingpreviousknowledge.Table4showstheresults obtainedafterthetest.EachcolumninTable4representsthesub- typesdetected.EachrowinTable4representsthetotalnumber ofelementsbelongingtoeachoftheclasses,thetotalnumberof elementsassignedtothatclass,andthenumberofmisclassified elements.Wecanseethatthenumberoferrorsdecreasesasmore casesareincorporatedintothesystem.

6. Conclusions

MicroCBRisaspecializedandnovelsystemthatintegratesthe stepsofanexpressionanalysiswithinthestagesofaCBRcycle.

MicroCBRisabletoincorporatetheknowledgeacquiredinprevious classificationsanduseittoperformnewclassifications,providinga muchappreciateddecisionsupporttoolfordoctors.Theuseofthe CBRsystemfacilitatestheselectionandeliminationofprobesthat provideanomalousresults.Havingbeendiscardedintheretrieve phase,theseprobesarenotconsideredinsubsequentiterationsof thereasoningcycle.Asdemonstrated,theproposedsystemreduces thedimensionalitybasedonthefilteringofgeneswithlittlevari- abilityandthosethatdonotallowaseparationofindividualsdue tothedistributionofdata.Italsopresentsaclusteringtechnique basedontheneuronalnetworkESOINN,whichisvalidatedwith aPAMtechnique.Finally,MicroCBRincorporatesatechniquefor knowledgeextractionandpresentsittothehumanexpertsina veryintuitiveformat.

Withtheresultsobtainedfromempiricalstudieswecancon- cludethatMicroCBRprovidesatoolthatdetectsgenesandprobes, whicharethemostimportantfactorforthedetectionofpathol- ogy,andfacilitatesaclassificationandreliablediagnosis,asshown bytheresultspresentedinthispaper.MicroCBRhasbeenapplied toclassifyCLLleukemiapatientsandallowsthehumanexpertto obtaininformationabouttheclassificationprocess andtoiden- tifytheprobesconsideredasimportantorirrelevantforfurther classifications.

Acknowledgment

SpecialthankstotheInstituteofCancerofSalamancaforthe informationandtechnologyprovided.

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