Thisbookisrecommendedforuseassupplementarymaterialbothinsideandoutside classroomsorasaself-learningguideforstudents,scientists,andresearcherswhodealwith numericdatainmolecularbiologyandrelated?elds. Thosewhostartasbeginners,but desiretobeatanintermediatelevel,will?ndthisbookespeciallyusefulintheirlearning pathway. WewanttothankJohnWalker(serieseditor),PatrickMarton,DavidCasey,andAnne Meagher,(editorsatSpringerandHumana)andShanthyJaganathan(Integra-India). The followingpersonsprovidedusefuladviceandcommentsonselectionoftopics,referralto expertsineachtopic,and/orchapterreviewsthatwetrulyappreciate:StephenLooney(a former editor of this book), Stan Young, Dmitri Zaykin, Douglas Hawkins, Wei Pan, Alexandre Almeida, John Ho, Rebecca Doerge, Paula Trushin, Kevin Morgan, Jason Osborne,PeterWestfall,JennyXiang,Ya-linChiu,YolandaBarron,HuiboShao,Alvin Mushlin,andRonaldFanta. Drs. Bang,Zhou,andMazumdarwerepartiallysupported byClinicalTranslationalScienceCenter(CTSC)grant(UL1-RR024996). HeejungBang vii Contents Preface. vii Contributors. xi PARTIBASICSTATISTICS. 1 1. ExperimentalStatisticsforBiologicalSciences. 3 HeejungBangandMarieDavidian 2. NonparametricMethodsforMolecularBiology. 105 KnutM. WittkowskiandTingtingSong 3. BasicsofBayesianMethods. 155 SujitK. Ghosh 4. TheBayesiant-TestandBeyond. 179 MithatGönen PARTII DESIGNSANDMETHODSFORMOLECULARBIOLOGY. 201 5. SampleSizeandPowerCalculationforMolecularBiologyStudies. 203 Sin-HoJung 6. DesignsforLinkageAnalysisandAssociationStudiesofComplexDiseases. 219 YuehuaCui,GengxinLi,ShaoyuLi,andRonglingWu 7. IntroductiontoEpigenomicsandEpigenome-WideAnalysis. 243 MelissaJ. FazzariandJohnM. Greally 8. Exploration,Visualization,andPreprocessingofHigh–DimensionalData. 267 ZhijinWuandZhiqiangWu PARTIII STATISTICALMETHODSFORMICROARRAYDATA. 285 9. IntroductiontotheStatisticalAnalysisofTwo-ColorMicroarrayData. 287 MartinaBremer,EdwardHimelblau,andAndreasMadlung 10. BuildingNetworkswithMicroarrayData. 315 BradleyM. Broom,WareeRinsurongkawong,LajosPusztai, andKim-AnhDo PARTIV ADVANCEDORSPECIALIZEDMETHODSFORMOLECULARBIOLOGY. 345 11. SupportVectorMachinesforClassi?cation:AStatisticalPortrait. 347 YoonkyungLee 12. AnOverviewofClusteringAppliedtoMolecularBiology. 369 RebeccaNugentandMarinaMeila ix xContents 13. HiddenMarkovModelandItsApplicationsinMotifFindings. 405 JingWuandJunXie 14. DimensionReductionforHigh-DimensionalData. 417 LexinLi 15. IntroductiontotheDevelopmentandValidationofPredictiveBiomarker ModelsfromHigh-ThroughputDataSets. 435 XutaoDengandFabienCampagne 16. Multi-geneExpression-basedStatisticalApproachestoPredicting Patients’ClinicalOutcomesandResponses. 471 FengCheng,Sang-HoonCho,andJaeK. Lee 17. Two-StageTestingStrategiesforGenome-WideAssociationStudies inFamily-BasedDesigns. 485 AmyMurphy,ScottT. Weiss,andChristophLange 18. StatisticalMethodsforProteomics. 497 KlausJung PARTVMETA-ANALYSISFORHIGH-DIMENSIONALDATA. 509 19. StatisticalMethodsforIntegratingMultipleTypesofHigh-ThroughputData. 511 YangXieandChulAhn 20. ABayesianHierarchicalModelforHigh-DimensionalMeta-analysis. 531 FeiLiu 21. MethodsforCombiningMultipleGenome-WideLinkageStudies. 541 TreciaA. KippolaandStephanieA. Santorico PARTVI OTHERPRACTICALINFORMATION. 561 22. ImprovedReportingofStatisticalDesignandAnalysis:Guidelines, Education,andEditorialPolicies. 563 MadhuMazumdar,SampritBanerjee,andHeatherL. VanEpps 23. StataCompanion. 599 JenniferSousaBrennan SubjectIndex. 627 Contributors CHULAHN• Division of Biostatistics, Department of Clinical Sciences, The Harold C.
Bang / Zhou / van Epps
Statistical Methods in Molecular Biology jetzt bestellen!
Zielgruppe
Professional/practitioner
Weitere Infos & Material
Basic Statistics.- Experimental Statistics for Biological Sciences.- Nonparametric Methods for Molecular Biology.- Basics of Bayesian Methods.- The Bayesian t-Test and Beyond.- Designs and Methods for Molecular Biology.- Sample Size and Power Calculation for Molecular Biology Studies.- Designs for Linkage Analysis and Association Studies of Complex Diseases.- to Epigenomics and Epigenome-Wide Analysis.- Exploration, Visualization, and Preprocessing of High–Dimensional Data.- Statistical Methods for Microarray Data.- to the Statistical Analysis of Two-Color Microarray Data.- Building Networks with Microarray Data.- Advanced or Specialized Methods for Molecular Biology.- Support Vector Machines for Classification: A Statistical Portrait.- An Overview of Clustering Applied to Molecular Biology.- Hidden Markov Model and Its Applications in Motif Findings.- Dimension Reduction for High-Dimensional Data.- to the Development and Validation of Predictive Biomarker Models from High-Throughput Data Sets.- Multi-gene Expression-based Statistical Approaches to Predicting Patients’ Clinical Outcomes and Responses.- Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs.- Statistical Methods for Proteomics.- Meta-Analysis for High-Dimensional Data.- Statistical Methods for Integrating Multiple Types of High-Throughput Data.- A Bayesian Hierarchical Model for High-Dimensional Meta-analysis.- Methods for Combining Multiple Genome-Wide Linkage Studies.- Other Practical Information.- Improved Reporting of Statistical Design and Analysis: Guidelines, Education, and Editorial Policies.- Stata Companion.
