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外文资料--Using spectral components for predicting treatment.PDF

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外文资料--Using spectral components for predicting treatment.PDF

USINGSPECTRALCOMPONENTSFORPREDICTINGTREATMENTEFFECTSONTIMESERIESMICROARRAYGENEEXPRESSIONPROFILESQIANXUBIOENGINEERINGPROGRAMHKUSTCLEARWATERBAY,KOWLOON,HONGKONGEMAILFLEURXQUSTHKHONGXUEDEPTOFBIOCHEMISTRYHKUSTCLEARWATERBAY,KOWLOON,HONGKONGEMAILHXUEUSTHKQIANGYANGDEPTOFCOMPUTERSCIENCEANDENGINEERINGHKUSTCLEARWATERBAY,KOWLOON,HONGKONGEMAILQYANGCSEUSTHKABSTRACTANALYZINGTIMESERIESGENEEXPRESSIONPROFILESISANINCREASINGLYPOPULARMETHODFORUNDERSTANDINGTHEBEHAVIOROFAWIDERANGEOFBIOLOGICALSYSTEMSONECOULDSTUDYTHESTATUSOFADISEASEBYANALYZINGTHEINDUCTIONORREPRESSIONACTIVITYANDEFFECTSFROMANUMBERORASPECIFICGROUPOFGENESINSUCHASCENARIO,ITISOFTENNATURALFORBIOLOGICALRESEARCHERSTOPOSEOUTTHEQUESTIONOFWHETHERONECOULDPREDICTTHETREATMENTEFFECTSBYUSINGSUCHTIMESERIESMICROARRAYGENEEXPRESSIONPROFILESHOWEVER,SUCHPROBLEMISABIGCHALLENGECONSIDERINGTHEIRSPECIFICNATUREUSUALLYSUCHTIMESERIESGENEEXPRESSIONPROFILESARESHORTANDTHESAMPLINGRATESARENOTUNIFORMOUREXPERIMENTSWITHAREALWORLDDATASETSHOWTHATTRADITIONALMACHINELEARNINGMETHODSSUCHASSUPPORTVECTORMACHINEWILLNOTPERFORMWELLINSUCHACASEINTHISPAPER,WEDECOMPOSEATIMESERIESGENEEXPRESSIONPROFILEINTOFREQUENCYCOMPONENTSANDAPPLYMACHINELEARNINGALGORITHMSTOHELPIMPROVETHEPREDICTIONACCURACYEXPERIMENTALRESULTSSHOWTHATOURALGORITHMISBOTHACCURATEANDEFFECTIVEKEYWORDSTIMESERIESGENEEXPRESSION;TREATMENTPREDICTION;SPECTRALCOMPONENTSIINTRODUCTIONGENEEXPRESSIONISTHEPROCESSBYWHICHINHERITABLEINFORMATIONFROMAGENEISMADEINTOAFUNCTIONALGENEPRODUCT,SUCHASPROTEINORRNAWHILEINTHEFIELDOFMOLECULARBIOLOGY,GENEEXPRESSIONPROFILINGISTHEMEASUREMENTOFTHEACTIVITYOFTHOUSANDSOFGENESATONCETHEREFORE,AGLOBALPICTUREOFCELLULARFUNCTIONCANBECREATEDANDANALYZEDVIATHEEXPRESSIONPROFILESFORMICROARRAYTECHNOLOGY,ITMEASURESTHERELATIVEACTIVITYOFPREVIOUSLYIDENTIFIEDTARGETGENESTIMESERIESEXPRESSIONPROFILING,DIFFERENTFROMSTATICEXPRESSIONPROFILING,PROVIDESATEMPORALPROCESSOFTHEEXPRESSIONOFGENES,WHILESTATICEXPRESSIONPROFILINGOFGENESONLYPROVIDESASINGLESNAPSHOTOFTHEGENESRELATEDFORTIMESERIESDATA,ITISINTUITIVETOSEETHATTHESUCCESSIVEPOINTSARENOTINDEPENDENTIDENTICALLYDISTRIBUTED,HENCE,WHENANALYZINGSUCHDATA,UNDERSTANDINGTHECORRELATIONBETWEENTHESUCCESSIVEDATAPOINTSISALSOVERYIMPORTANTONEOFTHEAPPLICATIONSOFANALYZINGTIMESERIESMICROARRAYGENEEXPRESSIONDATAISTOPREDICTTREATMENTEFFECTSFORMANYCHRONICDISEASES,TREATMENTEFFECTSAREOFTENNONNEGLIGIBLEANDTHESIDEEFFECTSCAUSEDBYIMPROPERTREATMENTAREVERYSERIOUSFOREXAMPLE,OURDATASETUSEDFORANALYZINGINTHISPAPERREFLECTSTHETREATMENTEFFECTSOFINTERFERONANDRIBAVIRINTOHCVHEPATITISCVIRUSINFECTEDPATIENTSHCVISONEOFTHECAUSESOFCHRONICHEPATITIS,CIRRHOSIS,ANDHEPATOCELLULARCARCINOMATHECURRENTMETHODOFHCVTREATMENTISACOMBINATIONOFPEGYLATEDINTERFERONALPHAANDTHEANTIVIRALDRUGRIBAVIRINFOR24OR48WEEKSNEVERTHELESS,USINGTHESETWOKINDSOFDRUGSTOGETHERMAYLEADTOSIDEEFFECTSASTHEPATIENTSMAYGETHEADACHESOREVENMYELOIDDISORDERSANDNEUROPSYCHIATRICSYMPTOMSTHEREFORE,ITISNATURALTOASKTHEQUESTIONABOUTWHETHERWECOULDPREDICTTHETREATMENTEFFECTSATANEARLYSTAGE,INSTEADOFAFTER24OR48WEEKSWHENTHEPATIENTSMAYALREADYHAVESHOWNTHESYMPTOMSOFSIDEEFFECTSHOWEVER,CONVENTIONALMETHODSINBIOLOGYCANNOTHANDLESUCHPROBLEMSINASATISFYINGWAYIN1,ITISSUGGESTEDTHATMACHINELEARNINGMETHODSCOULDHELPPREDICTTHETREATMENTEFFECTSOFTIMESERIESMICROARRAYGENEEXPRESSIONPROFILESSUCCESSFULANALYSISANDCOMPREHENSIONOFWHATWASHIDDENBEHINDTHESEGENEEXPRESSIONPROFILESISANIMPORTANTPROBLEMINBIOINFORMATICSANDMANYRESEARCHERSHAVEPROPOSEDVARIOUSALGORITHMSFORANALYZINGGENEEXPRESSIONEARLIERWORKONANALYZINGTIMESERIESGENEEXPRESSIONDATAFREQUENTLYUSEDMETHODSTHATARETHESAMEFORSTATICEXPRESSION2LATER,ALGORITHMSWEREDEVELOPEDFORSPECIFICALLYTARGETINGTIMESERIESDATA3HOWEVER,TIMESERIESDATAHAVEMANYSPECIFICCHALLENGESSINCEIT’SVERYEXPENSIVETOPERFORMTIMESERIESEXPERIMENTS,MANYTIMESERIESAREVERYSHORTITISSHOWNIN4THATMORETHAN80OFALLTIMESERIESDATASETSINSTANFORDMICROARRAYDATABASESMDCONTAINLESSTHAN8TIMEPOINTSTHENUMBEROFGENESTHATHAVEBEENPROFILEDISRATHERLARGE,USUALLYOVERTHOUSANDSTHECONFLICTBETWEENSUCHALARGENUMBEROFGENESANDTHESMALLTIMEPOINTSPOSESANEVENGREATERCHALLENGEFORANALYZINGSUCHTIMESERIESDATAANOTHERCHALLENGEISTHATOURSPECIFICPROBLEMOFPREDICTINGTHETREATMENTEFFECTSBASEDONMICROARRAYTIMESERIESGENEEXPRESSIONPROFILESISACLASSIFICATIONPROBLEM;NEVERTHELESSATPRESENT,ALARGENUMBEROFCURRENTRESEARCHINSTEADFOCUSESONCLUSTERINGMETHODSOFTHETIMESERIESDATA5,6EVENTHOUGHONECOULDTRYTOUSESOMEDENSITYBASEDCLUSTERING9781424447138/10/25002010IEEEMETHODSANDADAPTTHEMTOACLASSIFICATIONFRAMEWORK,MANYOFTHESECLUSTERINGALGORITHMSWILLOVERFITINOURCASE,WHENTHETIMEDATAPOINTSAREEXTREMELYSMALLTHEREFORE,ITISRATHERNECESSARYANDDIFFICULTTODESIGNANALGORITHMTOACCURATELYPREDICTTHETREATMENTEFFECTSOFSHORTTIMESERIESMICROARRAYGENEEXPRESSIONPROFILESTHEREHAVEALSOBEENMANYPREVIOUSRESEARCHONCLASSIFYINGGENEEXPRESSIONS,HOWEVER,MOSTOFTHESEMETHODSFOCUSONSTATICEXPRESSIONSUSINGSUPPORTVECTORMACHINES,FUREYETAL7CLASSIFIEDCANCERTISSUESAMPLESBICCIATOETAL8USEDPRINCIPALCOMPONENTANALYSISFORMULTICLASSCANCERANALYSISASPECIFICCHALLENGEFORTIMESERIESGENEEXPRESSIONCLASSIFICATION,ASPOINTEDOUTBY9,ISTHATTHEDISEASEDEVELOPMENTORTREATMENTRESPONSEISNOTUNIFORMANDISPATIENTSP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