MARC Record from marc_columbia

LEADER: 12233cam a2200397 a 4500
001 8041867
005 20221201052905.0
008 091211t20112011flua b 001 0 eng
010 $a 2009049470
020 $a9781420070170 (hardcover : alk. paper)
020 $a1420070177 (hardcover : alk. paper)
024 $a40018360827
035 $a(OCoLC)ocn166872488
035 $a(OCoLC)166872488
035 $a(NNC)8041867
035 $a8041867
040 $aDLC$cDLC$dBTCTA$dBAKER$dYDXCP$dCDX$dC#P$dOrLoB-B
050 00 $aQH324.2$b.D49 2011
082 00 $a570.285$222
245 00 $aBayesian modeling in bioinformatics /$cedited by Dipak K. Dey, Samiran Ghosh, Bani K. Mallick.
260 $aBoca Raton :$bCRC Press,$c[2011], ©2011.
300 $axxv, 440 pages :$billustrations ;$c25 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aChapman & Hall/CRC biostatistics series ;$v34
505 00 $g1.$tEstimation and Testing in Time-Course Microarray Experiments /$rM. Pensky -- $g1.1.$tAbstract -- $g1.2.$tIntroduction -- $g1.3.$tData Structure -- $g1.3.1.$tData Structure in the One-Sample Case -- $g1.3.2.$tData Structure in the Multi-Sample Case -- $g1.4.$tStatistical Methods for the One-Sample Microarray Experiments -- $g1.4.1.$tMultivariate Bayes Methodology -- $g1.4.2.$tFunctional Data Approach -- $g1.4.3.$tEmpirical Bayes Functional Data Approach -- $g1.5.$tStatistical Methods for the Multi-Sample Microarray Experiments -- $g1.5.1.$tMultivariate Bayes Methodology in the Two-Sample Case -- $g1.5.2.$tEstimation and Testing in Empirical Bayes Functional Data Model -- $g1.5.3.$tBayesian Hierarchical Model with Subject-Specific Effect -- $g1.5.4.$tHidden Markov Models -- $g1.6.$tSoftware -- $g1.7.$tComparisons between Techniques -- $g1.8.$tDiscussion -- $tReferences -- $g2.$tClassification for Differential Gene Expression Using Bayesian Hierarchical Models /$rAlex Lewin -- $g2.1.$tIntroduction -- $g2.2.$tBayesian Hierarchical Model for Differential Expression -- $g2.2.1.$tNormalization -- $g2.2.2.$tShrinkage of Gene Variances -- $g2.3.$tDifferential Expression between Two Conditions -- $g2.3.1.$tClassification Framework -- $g2.3.2.$tMixture Model -- $g2.3.3.$tUnstructured Prior, Fixed Interval Null -- $g2.3.4.$tUnstructured Prior, Adaptive Interval Null -- $g2.3.5.$tUnstructured Prior, Bayesian Decision Rule, Point Null -- $g2.3.6.$tUnstructured Prior, Point Null and Marginal P-Values -- $g2.3.7.$tDiscussion -- $g2.4.$tTesting Hypotheses for Multiclass Data -- $g2.5.$tPredictive Model Checking -- $g2.5.1.$tChecking Gene Variances -- $g2.5.2.$tChecking Mixture Prior -- $tReferences -- $g3.$tApplications of MOSS for Discrete Multi-Way Data /$rHelene Massam -- $g3.1.$tIntroduction -- $g3.2.$tMOSS -- $g3.3.$tConjugate Priors for Hierarchical Log-Linear Models -- $g3.3.1.$tModel Parameterization -- $g3.3.2.$tThe Multinomial for Hierarchical Log-Linear Models -- $g3.3.3.$tThe Diaconis-Ylvisaker Conjugate Prior -- $g3.3.4.$tComputing the Marginal Likelihood of a Regression -- $g3.4.$tDiscretizing Ordered Variables -- $g3.5.$tBayesian Model Averaging -- $g3.6.$tSimulated Examples -- $g3.6.1.$tFirst Simulated Example -- $g3.6.2.$tSecond Simulated Example -- $g3.7.$tReal Examples: Gene Expression -- $g3.7.1.$tBreast Cancer Data -- $g3.7.2.$tLeukemia Data -- $g3.7.3.$tLymph Node Data -- $g3.8.$tReal Examples: Genome-Wide Analysis of Estrogen Response with Dense SNP Array Data -- $g3.9.$tDiscussion -- $tReferences -- $g4.$tNonparametric Bayesian Bioinformatics /$rDavid B. Dunson -- $g4.1.$tIntroduction -- $g4.2.$tDirichlet Process Mixture Models -- $g4.3.$tMultiple Testing and High-Dimensional Regression -- $g4.4.$tClustering and Functional Data Analysis -- $g4.5.$tAdditional Topics -- $g4.6.$tDiscussion -- $tReferences -- $g5.$tMeasurement Error and Survival Model for cDNA Micro-arrays /$rJoseph G. Ibrahim -- $g5.1.$tIntroduction -- $g5.2.$tThe Data Structure -- $g5.3.$tThe General Model -- $g5.4.$tPriors -- $g5.5.$tModel Fit -- $g5.6.$tCase Study in Breast Cancer -- $g5.6.1.$tEstimating the Measurement Error Parameters -- $g5.6.2.$tData Preprocessing -- $g5.6.3.$tResults: Genes Identified by the Gene Only Model -- $g5.7.$tRobustness Analysis and Operating Characteristics -- $g5.7.1.$tDeviation from Normality in the Data -- $g5.7.2.$tSimulations Demonstrating Robustness to Nonnormality -- $g5.8.$tDiscussion -- $tReferences -- $tAppendix -- $g6.$tBayesian Robust Inference for Differential Gene Expression /$rRaphael Gottardo -- $g6.1.$tIntroduction -- $g6.2.$tModel and Prior Distributions -- $g6.3.$tParameter Estimation -- $g6.4.$tApplication to Experimental Data -- $g6.4.1.$tData Description -- $g6.4.2.$tResults -- $g6.5.$tDiscussion -- $tReferences -- $g7.$tBayesian Hidden Markov Modeling of Array CGH Data /$rSubharup Guha -- $g7.1.$tIntroduction -- $g7.2.$tA Bayesian Model -- $g7.3.$tCharacterizing Array CGH Profiles -- $g7.3.1.$tClassification Scheme -- $g7.3.2.$tPosterior Inference -- $g7.4.$tIllustrations -- $g7.4.1.$tPancreatic Adenocarcinoma Data -- $g7.4.2.$tComparisons with Some Existing Methods -- $g7.5.$tSimulation Studies -- $g7.5.1.$tComparison with Non-Bayesian HMM and CBS Algorithms -- $g7.5.2.$tPrior Sensitivity -- $g7.6.$tConclusion -- $g7.7.$tAppendix: An MCMC Algorithm -- $tReferences -- $g8.$tBayesian Approaches to Phylogenetic Analysis /$rRafe M. Brown -- $g8.1.$tBackground -- $g8.1.1.$tCalculating the Likelihood for a Phylogenetic Model -- $g8.1.2.$tMaximum Likelihood Approaches -- $g8.2.$tBayesian Version of "Standard" Model-Based Phylogenetics -- $g8.3.$tCurrent Directions in Model-Based Phylogenetics -- $g8.3.1.$tModeling Variability in the Rate of Sequence Substitution -- $g8.3.2.$tModeling Variability in Rates of Different Types of Substitution -- $g8.3.3.$tAllowing Process of Molecular Evolution to Change over the Tree -- $g8.3.4.$tContext-Dependent Models -- $g8.4.$tIntegration with Structured Coalescent -- $g8.5.$tDivergence Time Estimation -- $g8.6.$tSimultaneous Alignment and Inference -- $g8.7.$tExample Analysis -- $g8.8.$tConclusion -- $tReferences -- $g9.$tGene Selection for the Identification of Biomarkers in High-Throughput Data /$rLajos Pusztai -- $g9.1.$tIntroduction -- $g9.2.$tVariable Selection in Linear Settings -- $g9.2.1.$tContinuous Response -- $g9.2.2.$tCategorical Response -- $g9.2.3.$tSurvival Time -- $g9.2.4.$tMarkov Chain Monte Carlo Algorithm -- $g9.2.5.$tPosterior Inference -- $g9.3.$tModel-Based Clustering -- $g9.3.1.$tFinite Mixture Models -- $g9.3.2.$tDirichlet Process Mixture Models -- $g9.3.3.$tPrior Setting -- $g9.3.4.$tMCMC Implementation -- $g9.3.5.$tPosterior Inference -- $g9.4.$tBreast Cancer DNA Microarrays -- $g9.4.1.$tExperimental Design -- $g9.4.2.$tPre-Processing -- $g9.4.3.$tClassification via Bayesian Variable Selection -- $g9.5.$tConclusion -- $tReferences -- $g10.$tSparsity Priors for Protein-Protein Interaction Predictions /$rHongyu Zhao -- $g10.1.$tIntroduction -- $g10.2.$tModel -- $g10.2.1.$tExample -- $g10.3.$tA Bayesian Approach -- $g10.4.$tReal Data Analysis -- $g10.5.$tConclusion and Discussion -- $tReferences -- $g11.$tLearning Bayesian Networks for Gene Expression Data /$rFaming Liang -- $g11.1.$tIntroduction -- $g11.2.$tBayesian Networks -- $g11.3.$tLearning Bayesian Networks Using SAMC -- $g11.3.1.$tA Review of the SAMC Algorithm -- $g11.3.2.$tLearning Bayesian Networks Using SAMC -- $g11.4.$tNumerical Examples -- $g11.4.1.$tA Simulated Example -- $g11.4.2.$tThe Yeast Cell Cycle Data -- $g11.5.$tDiscussion -- $tReferences -- $g12.$tIn-Vitro to In-Vivo Factor Profiling in Expression Genomics /$rMike West -- $g12.1.$tIntroduction -- $g12.2.$tModeling Gene Expression -- $g12.2.1.$tSparsity Priors -- $g12.2.2.$tSignature Scores -- $g12.2.3.$tSparse, Semi-Parametric Latent Factor Models -- $g12.3.$tSignature Factor Profiling Analysis -- $g12.4.$tThe E2F3 Signature in Ovarian, Lung, and Breast Cancer -- $g12.4.1.$tIndications of E2F3 Co-Regulation with NF-Y -- $g12.4.2.$tAdenocarcinoma versus Squamous Cell Carcinoma -- $g12.4.3.$tSurvival and the E2F3 Pathway -- $g12.5.$tClosing Comments -- $tReferences -- $g13.$tProportional Hazards Regression Using Bayesian Kernel Machines /$rBani K. Mallick -- $g13.1.$tIntroduction -- $g13.2.$tBackground and Framework -- $g13.2.1.$tReproducing Kernel Hilbert Space -- $g13.2.2.$tSupport Vector Machine -- $g13.2.3.$tRelevance Vector Machine -- $g13.3.$tProportional Hazards Model Based on Kernel Regression -- $g13.3.1.$tModeling of p(t\W) and the Baseline Hazard Function -- $g13.3.2.$tModeling of p(W\f) and f Using RKHS -- $g13.4.$tRelevance Vector Machine-Based PH Regression -- $g13.4.1.$tConditional Distribution and Posterior Sampling -- $g13.5.$tSupport Vector Machine-Based PH Regression -- $g13.6.$tPrediction -- $g13.7.$tApplication -- $g13.7.1.$tBreast Carcinomas Data Set -- $g13.7.2.$tDiffuse Large B-cell Lymphoma (DLBCL) Data Set -- $g13.7.3.$tModel Comparison -- $g13.8.$tDiscussion -- $tReferences -- $g14.$tA Bayesian Mixture Model for Protein Biomarker Discovery /$rRaj Bandyopadhyay -- $g14.1.$tIntroduction --
500 $a"A Chapman & Hall book."
504 $aIncludes bibliographical references and index.
505 80 $g14.1.1.$tBackground -- $g14.1.2.$tStatistical Methods for Classification of Mass Spectrometry Data -- $g14.2.$tThe Data -- $g14.3.$tLikelihood Based Inference for Proteomic Spectra -- $g14.4.$tA Hierarchical Beta Mixture Model -- $g14.5.$tPosterior Inference -- $g14.6.$tResults -- $g14.7.$tDiscussion -- $tReferences -- $g15.$tBayesian Methods for Detecting Differentially Expressed Genes /$rLynn Kuo -- $g15.1.$tIntroduction -- $g15.2.$tModels for Microarray Gene Expression Data -- $g15.2.1.$tNormal Model -- $g15.2.2.$tLinear Model -- $g15.2.3.$tGamma Model -- $g15.3.$tPrior Elicitation -- $g15.3.1.$tTraditional Conjugate Prior -- $g15.3.2.$tPower Prior -- $g15.3.3.$tMixture Non-Parametric Prior -- $g15.4.$tGene Selection Algorithms -- $g15.4.1.$tOrdered Bayes Factor -- $g15.4.2.$tCalibrated Bayes Factor -- $g15.4.3.$tTwo-Criterion -- $g15.4.4.$tBH Method -- $g15.4.5.$tBayesian FDR -- $g15.4.6.$tPosterior Odds -- $g15.5.$tA Simulation Study -- $g15.6.$tReal Data Example -- $g15.7.$tDiscussion -- $g15.7.1.$tBimodal Data -- $g15.7.2.$tTime Course Data -- $tReferences -- $g16.$tBayes and Empirical Bayes Methods for Spotted Microarray Data Analysis /$rDabao Zhang -- $g16.1.$tIntroduction -- $g16.2.$tMultiplicative Background Correction -- $g16.3.$tBayesian Normalization -- $g16.3.1.$tMeasurement-Error Models -- $g16.3.2.$tBayesian Framework -- $g16.3.3.$tIdentifying Differentially Expressed Genes -- $g16.4.$tGeneralized Empirical Bayes Method for Multiple Arrays -- $g16.4.1.$tGeneralized Empirical Bayes Estimators -- $g16.4.2.$tIdentifying Differentially Expressed Genes -- $g16.4.3.$tReducing ChIP-Chip Data -- $g16.5.$tSoftware Packages -- $g16.5.1.$tMicroBayes -- $g16.5.2.$tGEBCauchy -- $tReferences -- $g17.$tBayesian Classification Method for QTL Mapping /$rMin Zhang -- $g17.1.$tIntroduction -- $g17.1.1.$tNon-Bayesian Methods -- $g17.1.2.$tBayesian Methods -- $g17.2.$tBayesian Classification to Map QTL -- $g17.2.1.$tModel and Prior Specification -- $g17.2.2.$tBayesian Inference and the Two-Stage Procedure -- $g17.3.$tQTLBayes: Software for QTL Mapping with Bayesian Classification -- $g17.3.1.$tAvailability -- $g17.3.2.$tModel, Bayesian Inference, and Algorithm -- $g17.3.3.$tInput Data and Format -- $g17.3.4.$tSpecifications and Default Setting -- $g17.3.5.$tOutput and Results -- $g17.3.6.$tSummary -- $g17.4.$tAn Example of Data Analysis Using QTLBayes -- $g17.4.1.$tThe Data -- $g17.4.2.$tQTL Analysis -- $g17.5.$tConclusion -- $g17.6.$tAcknowledgment -- $tReferences.
650 0 $aBioinformatics$xStatistical methods.
650 0 $aBayesian statistical decision theory.$0http://id.loc.gov/authorities/subjects/sh85012506
700 1 $aDey, Dipak.$0http://id.loc.gov/authorities/names/n98024536
700 1 $aGhosh, Samiran.$0http://id.loc.gov/authorities/names/n2009079269
700 1 $aMallick, Bani K.,$d1965-$0http://id.loc.gov/authorities/names/n00015175
830 0 $aChapman & Hall/CRC biostatistics series ;$v34.
852 00 $bsci$hQH324.2$i.D49 2011