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BIOINFORMATICSORIGINALPAPERVol.21no.152005,pages3301–3307doi:10.1093/bioinformatics/bti499

Dataandtextmining

Predictionerrorestimation:acomparisonofresampling

methods

AnnetteM.Molinaro1,3,∗,RichardSimon2andRuthM.Pfeiffer1

1BiostatisticsBranch,DivisionofCancerEpidemiologyandGeneticsand2BiometricResearchBranch,Divisionof

CancerTreatmentandDiagnostics,NCI,NIH,Rockville,MD20852USAand3DepartmentofEpidemiologyand

PublicHealth,YaleUniversitySchoolofMedicine,NewHaven,CT06520,USA

ReceivedonApril6,2005;revisedonApril28,2005;acceptedonMay12,2005

AdvanceAccesspublicationMay19,2005

ABSTRACT

Motivation:Ingenomicstudies,thousandsoffeaturesarecollected

onrelativelyfewsamples.Oneofthegoalsofthesestudiesistobuild

classifierstopredicttheoutcomeoffutureobservations.Thereare

threeinherentstepstothisprocess:featureselection,modelselection

andpredictionassessment.Withafocusonpredictionassessment,

wecompareseveralmethodsforestimatingthe‘true’predictionerror

ofapredictionmodelinthepresenceoffeatureselection.

Results:Forsmallstudieswherefeaturesareselectedfromthou-

sandsofcandidates,theresubstitutionandsimplesplit-sampleestim-

atesareseriouslybiased.Inthesesmallsamples,leave-one-out

cross-validation(LOOCV),10-foldcross-validation(CV)andthe.632+

bootstraphavethesmallestbiasfordiagonaldiscriminantanalysis,

nearestneighborandclassificationtrees.LOOCVand10-foldCVhave

thesmallestbiasforlineardiscriminantanalysis.Additionally,LOOCV,

5-and10-foldCV,andthe.632+bootstraphavethelowestmean

squareerror.The.632+bootstrapisquitebiasedinsmallsample

sizeswithstrongsignal-to-noiseratios.Differencesinperformance

amongresamplingmethodsarereducedasthenumberofspecimens

availableincrease.

Contact:annette.molinaro@yale.edu

SupplementaryInformation:Acompletecompilationofresultsand

RcodeforsimulationsandanalysesareavailableinMolinaroetal.

(2005)(http://linus.nci.nih.gov/brb/TechReport.htm).

1INTRODUCTION

Ingenomicexperimentsonefrequentlyencountershighdimensional

dataandsmallsamplesizes.Microarrayssimultaneouslymonitor

expressionlevelsforseveralthousandsofgenes.Proteomicprofil-

ingstudiesusingSELDI-TOF(surface-enhancedlaserdesorption

andionizationtime-of-flight)measuresizeandchargeofproteins

andproteinfragmentsbymassspectroscopy,andresultinupto

15000intensitylevelsatprespecifiedmassvaluesforeachspectrum.

Samplesizesinsuchexperimentsaretypically<100.

Inmanystudies,observationsareknowntobelongtopredeter-

minedclassesandthetaskistobuildpredictorsorclassifiersfor

newobservationswhoseclassisunknown.Decidingwhichgenesor

proteomicmeasurementstoincludeinthepredictioniscalledfea-

tureselectionandisacrucialstepindevelopingaclasspredictor.

∗Towhomcorrespondenceshouldbeaddressed.Includingtoomanynoisyvariablesreducesaccuracyofthepredic-

tionandmayleadtoover-fittingofdata,resultinginpromisingbut

oftennon-reproducibleresults(Ransohoff,2004).

Anotherdifficultyismodelselectionwithnumerousclassification

modelsavailable.Animportantstepinreportingresultsisassessing

thechosenmodel’serrorrate,orgeneralizability.Intheabsenceof

independentvalidationdata,acommonapproachtoestimatingpre-

dictiveaccuracyisbasedonsomeformofresamplingtheoriginal

data,e.g.cross-validation.Thesetechniquesdividethedataintoa

learningsetandatestset,andrangeincomplexityfromthepopu-

larlearning-testsplittov-foldcross-validation,Monte-Carlov-fold

cross-validationandbootstrapresampling.Fewcomparisonsof

standardresamplingmethodshavebeenperformedtodate,andallof

themexhibitlimitationsthatmaketheirconclusionsinapplicableto

mostgenomicsettings.Earlycomparisonsofresamplingtechniques

intheliteraturearefocussedonmodelselectionasopposedtopredic-

tionerrorestimation(BreimanandSpector,1992;Burman,1989).In

tworecentassessmentsofresamplingtechniquesforerrorestimation

(Braga-NetoandDougherty,2004;Efron,2004),featureselection

wasnotincludedaspartoftheresamplingprocedures,causingthe

conclusionstobeinappropriateforthehigh-dimensionalsetting.

Wehaveperformedanextensivecomparisonofresamplingmeth-

odstoestimatepredictionerrorusingsimulated(largesignal-to-noise

ratio),microarray(intermediatesignaltonoiseratio)andproteomic

data(lowsignal-to-noiseratio),encompassingincreasingsample

sizeswithlargenumbersoffeatures.Theimpactoffeatureselection

ontheperformanceofvariouscross-validationmethodsishigh-

lighted.Theresultselucidatethe‘best’resamplingtechniquesfor

futureresearchinvolvinghighdimensionaldatatoavoidoverly

optimisticassessmentoftheperformanceofamodel.

2METHODS

Inthepredictionproblem,oneobservesnindependentandidenticallydis-

tributed(i.i.d.)randomvariablesO1,...,OnwithunknowndistributionP.

EachobservationinOconsistsofanoutcomeYwithrangeYandanl-vector

ofmeasuredcovariates,orfeatures,XwithrangeX,suchthatOi=(Xi,Yi),

i=1,...,n.InmicroarrayexperimentsXincludesgeneexpressionmeas-

合集下载

Current Protocols in Bioinformatics

Current Protocols in Bioinformatics

cc: Liz Miranker, Allen Ranz, Gary Stormo

Scott Holmes, Associate Production Manager, John Wiley & Sons, Inc. (201) 748-6272 ⋅ FAX: (201) 748-6207 ⋅ sholmes@ Current Protocols in Bioinformatics

April 7, 2005

Dr. Michael Q. Zhang

Cold Spring Harbor Laboratory

1 Bungtown Road

Cold Spring Harbor, NY 11724 Tel: (516) 367-8393

Fax: (516) 367-8461 mzhang@

RE: CPBI Unit 2.9 rough pages

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深度基因组测序流程

深度基因组测序流程

深度基因组测序流程

英文回答：

Deep genome sequencing, also known as whole genome

sequencing, is a comprehensive approach to analyze an

individual's entire DNA sequence. It involves the

determination of the nucleotide sequence of all the DNA in

a person's genome, including both coding and non-coding

regions. This process provides a detailed blueprint of an

individual's genetic makeup and can be used to identify

genetic variations, mutations, and potential disease-causing genes.

The deep genome sequencing process begins with the

collection of a DNA sample, typically obtained from blood

or saliva. The DNA is then isolated and purified to remove

any contaminants. Next, the DNA is fragmented into smaller

pieces and subjected to library preparation, where adapters

are added to the DNA fragments to facilitate sequencing.

人源肿瘤异种移植模型的应用与进展

人源肿瘤异种移植模型的应用与进展

·综述与讲座·

人源肿瘤异种移植模型的应用与进展∗

∗　基金项目：国家十三五重大专项课题资助项目（２０１８ＺＸ０９２０１０１３）；防治重大疾病创新药国际标准化临床评价示范性技术平台建设资助项目１　３０００７１　南开大学医学院２　通讯作者，Ｅ⁃ｍａｉｌ：ｊｉａｏｓｃ＠ｖｉｐ．ｓｉｎａ．ｃｏｍ１００８５３　北京　解放军总医院第一医学中心肿瘤内科杨子仲１，张国庆，刘茗露，孙　琼，邵佳康，焦顺昌１，２

【摘　要】　人源肿瘤异种移植（ＰＤＸ）模型是一种将患者来源的新鲜肿瘤组织移植到免疫缺陷小鼠体内构建的新型肿瘤动物模型。ＰＤＸ模型可以充分保留患者的肿瘤微环境，维持原发瘤的突变基因与异质性，在肿瘤机制研究和临床药物筛选中得到了广泛应用，也得以不断改进。本文就ＰＤＸ技术近年来在耐药研究及生物信息技术等领域的新应用进行简述。同时结合传统ＰＤＸ模型的局限性，介绍３种由ＰＤＸ模型发展而来的新型异种移植瘤模型，即微型异种移植（ＭｉｎｉＰＤＸ）模型、人源化异种移植（Ｈｕ⁃ＰＤＸ）模型和原位异种移植（ＰＤＯＸ）模型的特点及应用，并对ＰＤＸ模型的发展方向进行展望。 【关键词】　恶性肿瘤；　人源肿瘤异种移植（ＰＤＸ）；　人源化小鼠；　精准治疗中图分类号：Ｒ７３０􀆰５ 文献标识码：Ａ 文章编号：１００９⁃０４６０（２０２０）１１⁃１０３１⁃０６

Ｄｅｖｅｌｏｐｍｅｎｔａｎｄａｐｐｌｉｃａｔｉｏｎｏｆｐａｔｉｅｎｔ⁃ｄｅｒｉｖｅｄｔｕｍｏｒｘｅｎｏｇｒａｆｔｍｏｄｅｌｓ ＹＡＮＧＺｉｚｈｏｎｇ，ＺＨＡＮＧＧｕｏｑｉｎｇ，ＬＩＵＭｉｎｇｌｕ，ＳＵＮＱｉｏｎｇ，ＳＨＡＯＪｉａｋａｎｇ，ＪＩＡＯＳｈｕｎｃｈａｎｇ．ＤｅｐａｒｔｍｅｎｔｏｆＯｎｃｏｌｏｇｙ，ｔｈｅＦｉｒｓｔＭｅｄｉｃａｌＣｅｎｔｅｒ，ＣｈｉｎｅｓｅＰＬＡＧｅｎｅｒａｌＨｏｓｐｉｔａｌ，Ｂｅｉｊｉｎｇ１００８５３，ＣｈｉｎａＣｏｒｒｅｓｐｏｎｄｉｎｇａｕｔｈｏｒ：ＪＩＡＯＳｈｕｎｃｈａｎｇ，Ｅ⁃ｍａｉｌ：ｊｉａｏｓｃ＠ｖｉｐ．ｓｉｎａ．ｃｏｍ 【Ａｂｓｔｒａｃｔ】　Ｐａｔｉｅｎｔ⁃ｄｅｒｉｖｅｄｘｅｎｏｇｒａｆｔ（ＰＤＸ）ｍｏｄｅｌｉｓａｎｏｖｅｌａｎｉｍａｌｔｕｍｏｒｍｏｄｅｌｃｏｎｓｔｒｕｃｔｅｄｂｙｔｒａｎｓｐｌａｎｔｉｎｇｐａｔｉｅｎｔ⁃ｄｅｒｉｖｅｄｆｒｅｓｈｔｕｍｏｒｔｉｓｓｕｅｉｎｔｏｉｍｍｕｎｏｄｅｆｉｃｉｅｎｔｍｉｃｅ．Ｉｔｃａｎｆｕｌｌｙｐｒｅｓｅｒｖｅｔｈｅｔｕｍｏｒｍｉｃｒｏｅｎｖｉｒｏｎｍｅｎｔｏｆｐａｔｉｅｎｔｓ，ｗｈｉｃｈｈａｖｅｇｏｏｄｆｉｄｅｌｉｔｙｉｎｍａｉｎｔａｉｎｉｎｇｔｈｅｈｅｔｅｒｏｇｅｎｅｉｔｙａｎｄｍｕｔａｔｉｏｎｏｆｏｒｉｇｉｎａｌｓｐｅｃｉｍｅｎ．ＰＤＸｍｏｄｅｌｓｈａｖｅｂｅｅｎｗｉｄｅｌｙｕｓｅｄｉｎｔｈｅｃａｎｃｅｒｐａｔｈｏｇｅｎｅｓｉｓｓｔｕｄｉｅｓａｎｄｔｈｅｃｌｉｎｉｃａｌａｎｔｉｎｅｏｐｌａｓｔｉｃｄｒｕｇｓｓｅｌｅｃｔｉｏｎ．ＡｎｄｔｈｅｔｅｃｈｎｏｌｏｇｙｏｆＰＤＸｈａｓｂｅｅｎｃｏｎｔｉｎｕｏｕｓｌｙｉｍｐｒｏｖｅｄ．Ｉｎｔｈｉｓｐａｐｅｒ，ｗｅｃａｒｒｙｏｕｔａｒｅｖｉｅｗｏｎｔｈｅｎｏｖｅｌａｐｐｌｉｃａｔｉｏｎｓｏｆＰＤＸｉｎｄｒｕｇｒｅｓｉｓｔａｎｃｅｒｅｓｅａｒｃｈａｎｄｂｉｏｉｎｆｏｒｍａｔｉｃｓｔｏｏｌｓｄｅｖｅｌｏｐｍｅｎｔ．Ｍｏｒｅｏｖｅｒ，ｗｅｉｎｔｒｏｄｕｃｅｄｔｈｒｅｅａｄｖａｎｃｅｄｘｅｎｏｇｒａｆｔｍｏｄｅｌｓｔｈａｔｍａｋｅｓｕｐｆｏｒｔｈｅｄｅｆｉｃｉｅｎｃｙｏｆｃｏｎｖｅｎｔｉｏｎａｌＰＤＸ—ｍｉｎｉｐａｔｉｅｎｔ⁃ｄｅｒｉｖｅｄｘｅｎｏｇｒａｆｔ（ＭｉｎｉＰＤＸ）ｍｏｄｅｌｓ，ｈｕｍａｎｉｚｅｄｐａｔｉｅｎｔ⁃ｄｅｒｉｖｅｄｘｅｎｏｇｒａｆｔ（Ｈｕ⁃ＰＤＸ）ｍｏｄｅｌｓａｎｄｐａｔｉｅｎｔ⁃ｄｅｒｉｖｅｄｏｒｔｈｏｔｒｏｐｉｃｘｅｎｏｇｒａｆｔ（ＰＤＯＸ）ｍｏｄｅｌｓ．Ｆｉｎａｌｌｙ，ｗｅｐｒｏｓｐｅｃｔｔｈｅｆｕｔｕｒｅｒｅｓｅａｒｃｈｏｒｉｅｎｔａｔｉｏｎｓｏｆＰＤＸｍｏｄｅｌｉｎｔｈｅｃｌｉｎｉｃａｌｏｎｃｏｌｏｇｙ． 【ＫｅｙＷｏｒｄｓ】　Ｍａｌｉｇｎａｎｔｎｅｏｐｌａｓｍ；　Ｐａｔｉｅｎｔ⁃ｄｅｒｉｖｅｄｘｅｎｏｇｒａｆｔ（ＰＤＸ）；　Ｈｕｍａｎｉｚｅｄｍｏｕｓｅ；　Ｐｒｅｃｉｓｉｏｎｍｅｄｉｃｉｎｅ

英文论文投稿要求格式

英文论文投稿要求格式

The Journal features primary research in the following:

• Genomics and gene mapping

• Gene action, regulation and transmission

• Bioinformatics and computational genetics

• Molecular adaptation and selection

• Reproductive strategies and kinship analysis

• Quantitative genetics and Mendelian inheritance

• Molecular systematics and phylogenetics

• Population structure and phylogeography

• Conservation genetics and biodiversity

To be accepted for publication, a manuscript must make a significant

contribution to one or more of these subject areas and be of general

interest to the members of the American Genetic Association.

The Journal publishes

(1) Articles: important original research

(2) Brief Communications: short communications on current research,

important preliminary findings or new techniques