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ویرایش:
نویسندگان: Momiao Xiong
سری: Chapman and Hall/CRC mathematical & computational biology series
ISBN (شابک) : 9781315353418, 1498725783
ناشر: CRC Press
سال نشر: 2018
تعداد صفحات: 767
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 19 مگابایت
کلمات کلیدی مربوط به کتاب کلان داده در Omics و Imaging: تجزیه و تحلیل انجمن: بیومتری -- پردازش داده ها، سیستم های تصویربرداری در زیست شناسی -- روش های آماری، داده های بزرگ -- روش های آماری، بیومتری -- روش ها، ژنومیکس، مطالعات انجمن ژنتیکی، یادگیری ماشینی
در صورت تبدیل فایل کتاب Big Data in Omics and Imaging: Association Analysis به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب کلان داده در Omics و Imaging: تجزیه و تحلیل انجمن نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Big Data in Omics and Imaging: Association
Analysisبه توسعه اخیر تجزیه و تحلیل ارتباط و یادگیری
ماشین برای داده های ژنومی جمعیت و خانواده در دوره توالی یابی می
پردازد. این منحصر به فرد است که هم آزمایش فرضیه و هم یک رویکرد
داده کاوی را برای تشریح کلی ساختار ژنتیکی صفات پیچیده و طراحی
استراتژی های کارآمد برای پزشکی دقیق ارائه می دهد. چارچوبهای
کلی برای تجزیه و تحلیل ارتباط و یادگیری ماشین، که در متن ایجاد
شده است، میتواند برای دادههای ژنومی، اپی ژنومیک و تصویربرداری
اعمال شود.
ویژگیها
شکاف بین روشهای آماری سنتی و ابزارهای محاسباتی برای تجزیه و
تحلیل دادههای ژنتیکی و اپی ژنتیکی کوچک و روشهای آماری پیشرفته
مدرن برای دادههای بزرگ را پر میکند
ابزارهایی برای کاهش دادههای ابعادی بالا ارائه میکند
درباره الگوریتمهای جستجو برای انتخاب مدل و متغیر از جمله
الگوریتمهای تصادفیسازی، روشهای پروگزیمال و انتخاب زیرمجموعه
ماتریس بحث میکند
نمونههای دنیای واقعی و مطالعات موردی ارائه میدهد
یک وبسایت همراه با کد R خواهد داشت.
این کتاب برای دانشجویان کارشناسی ارشد و محققین ژنومیک،
بیوانفورماتیک و علوم داده طراحی شده است. این نشان دهنده تغییر
الگوی مطالعات ژنتیکی بیماری های پیچیده است - از تجزیه و تحلیل
ژنومی کم عمق به عمیق، از تجزیه و تحلیل داده های کم بعدی به
ابعاد بالا، چند متغیره به عملکردی با داده های توالی یابی نسل
بعدی (NGS) و از جمعیت های همگن به جمعیت ناهمگن و تجزیه و تحلیل
داده های شجره نامه موضوعات تحت پوشش عبارتند از: نظریه ماتریس
پیشرفته، الگوریتمهای بهینهسازی محدب، مدلهای رتبه پایین
تعمیمیافته، تکنیکهای تحلیل دادههای عملکردی، اصول یادگیری
عمیق و روشهای یادگیری ماشین برای ارتباط مدرن، تعامل، تحلیل
مسیر و شبکه انواع نادر و رایج، شناسایی نشانگرهای زیستی، خطر
بیماری و پیش بینی پاسخ دارویی.
Big Data in Omics and Imaging: Association
Analysisaddresses the recent development of
association analysis and machine learning for both population
and family genomic data in sequencing era. It is unique in that
it presents both hypothesis testing and a data mining approach
to holistically dissecting the genetic structure of complex
traits and to designing efficient strategies for precision
medicine. The general frameworks for association analysis and
machine learning, developed in the text, can be applied to
genomic, epigenomic and imaging data.
FEATURES
Bridges the gap between the traditional statistical methods and
computational tools for small genetic and epigenetic data
analysis and the modern advanced statistical methods for big
data
Provides tools for high dimensional data reduction
Discusses searching algorithms for model and variable selection
including randomization algorithms, Proximal methods and matrix
subset selection
Provides real-world examples and case studies
Will have an accompanying website with R code
The book is designed for graduate students and researchers in
genomics, bioinformatics, and data science. It represents the
paradigm shift of genetic studies of complex diseases- from
shallow to deep genomic analysis, from low-dimensional to high
dimensional, multivariate to functional data analysis with
next-generation sequencing (NGS) data, and from homogeneous
populations to heterogeneous population and pedigree data
analysis. Topics covered are: advanced matrix theory, convex
optimization algorithms, generalized low rank models,
functional data analysis techniques, deep learning principle
and machine learning methods for modern association,
interaction, pathway and network analysis of rare and common
variants, biomarker identification, disease risk and drug
response prediction.
Content: Mathematical FoundationSparsity-Inducing Norms, Dual Norms and Fenchel ConjugateSubdifferentialDefinition of SubgradientSubgradients of differentiable functionsCalculus of subgradientsProximal MethodsIntroductionBasics of Proximate MethodsProperties of the Proximal OperatorProximal AlgorithmsComputing the Proximal OperatorMatrix CalculusDerivative of a Function with Respect to a VectorDerivative of a Function with Respect to a MatrixDerivative of a Matrix with Respect to a ScalarDerivative of a Matrix with Respect to a Matrix or a VectorDerivative of a Vector Function of a VectorChain RulesWidely Used FormulaeFunctional Principal Component Analysis (FPCA)Principal Component Analysis (PCA)Basic Mathematical Tools for Functional Principal Component AnalysisUnsmoothed Functional Principal Component AnalysisSmoothed Principal Component AnalysisComputations for the Principal Component Function and the Principal Component ScoreCanonical Correlation AnalysisExercisesAppendix Linkage DisequilibriumConcepts of Linkage DisequilibriumMeasures of Two-locus Linkage DisequilibriumLinkage Disequilibrium Coefficient DNormalized Measure of Linkage DisequilibriumCorrelation Coefficient rComposite Measure of Linkage DisequilibriumThe Relationship Between the Measure of LD and Physical DistanceHaplotype ReconstructionClark\'s AlgorithmEM algorithmBayesian and Coalescence-based MethodsMulti-locus Measures of Linkage DisequilibriumMutual Information Measure of LDMulti-Information and Multi-locus Measure of LDJoint Mutual Information and a Measure of LD between a Marker and a Haplotype Block or Between Two Haplotype BlocksInteraction InformationConditional Interaction InformationNormalized Multi-InformationDistribution of Estimated Mutual Information, Multi-information and Interaction InformationCanonical Correlation Analysis Measure for LD between Two Genomic RegionsAssociation Measure between Two Genomic Regions Based on CCARelationship between Canonical Correlation and Joint InformationSoftware PackageBibliographical NotesAppendicesExercisesAssociation Studies for Qualitative TraitsPopulation-based Association Analysis for Common VariantsIntroductionThe Hardy-Weinberg EquilibriumGenetic ModelsOdds RatioSingle Marker Association AnalysisMulti-marker Association AnalysisPopulation-based Multivariate Association Analysis for Next-generation SequencingMultivariate Group TestsScore Tests and Logistic RegressionApplication of Score Tests for Association of Rare VariantsVariance-component Score Statistics and Logistic Mixed Effects ModelsPopulation-based Functional Association Analysis for Next-generation SequencingIntroductionFunctional Principal Component Analysis for Association TestSmoothed Functional Principal Component Analysis for Association TestSoftware PackageAppendicesExercisesAssociation Studies for Quantitative TraitsFixed Effect Model for a Single TraitIntroductionGenetic EffectsLinear Regression for a Quantitative TraitMultiple Linear Regression for a Quantitative TraitGene-based Quantitative Trait AnalysisFunctional Linear Model for a Quantitative TraitCanonical Correlation Analysis for Gene-based Quantitative Trait AnalysisKernel Approach to Gene-based Quantitative Trait AnalysisKernel and RKHSCovariance Operator and Dependence MeasureSimulations and Real Data AnalysisPower EvaluationApplication to Real Data ExamplesSoftware PackageAppendicesExercisesMultiple Phenotype Association StudiesPleiotropic Additive and Dominance EffectsMultivariate Marginal RegressionModelsEstimation of Genetic EffectsTest StatisticsLinear Models for Multiple Phenotypes and Multiple MarkersMultivariate Multiple Linear Regression ModelsMultivariate Functional Linear Models for Gene-based Genetic Analysis of Multiple PhenotypesCanonical Correlation Analysis for Gene-based Genetic Pleiotropic AnalysisMultivariate Canonical Correlation Analysis (CCA)Kernel CCAFunctional CCAQuadratically Regularized Functional CCADependence Measure and Association Tests of Multiple TraitsPrincipal Component for Phenotype Dimension ReductionPrincipal Component AnalysisKernel Principal Component AnalysisQuadratically Regularized PCA or Kernel PCAOther Statistics for Pleiotropic Genetics AnalysisSum of Squared Score TestUnified Score-based Association Test (USAT)Combining Marginal TestsFPCA-based Kernel Measure Test of IndependenceConnection between StatisticsSimulations and Real Data AnalysisType Error Rate and Power EvaluationApplication to Real Data ExampleSoftware PackageAppendices ExercisesFamily-based Association AnalysisGenetic Similarity and Kinship CoefficientsKinship CoefficientsIdentity CoefficientsRelation between identity coefficients and kinship coefficientEstimation of Genetic Relations from the DataGenetic Covariance between RelativesAssumptions and Genetic ModelsAnalysis for Genetic Covariance between RelativesMixed Linear Model for a Single TraitGenetic Random EffectMixed Linear Model for Quantitative Trait Association AnalysisEstimating Variance ComponentsHypothesis Test in Mixed Linear ModelsMixed Linear Models for Quantitative Trait Analysis with Sequencing DataMixed Functional Linear Models for Sequence-based Quantitative Trait AnalysisMixed Functional Linear Models (Type )Mixed Functional Linear Models (Type : Functional Variance Component Models)Multivariate Mixed Linear Model for Multiple TraitsMultivariate Mixed Linear ModelMaximum Likelihood Estimate of Variance ComponentsREML Estimate of Variance ComponentsHeritabilityHeritability Estimation for a Single TraitHeritability Estimation for Multiple TraitsFamily-based Association Analysis for Qualitative TraitThe Generalized T Test with Families and Additional Population StructuresCollapsing MethodCMC with FamiliesThe Functional Principal Component Analysis and Smooth Functional Principal Component Analysis with FamiliesSoftware PackageExerciseInteraction AnalysisMeasures of Gene-gene and Gene-environment Interaction for Qualitative TraitBinary Measure of Gene-gene and Gene-environment InteractionDisequilibrium Measure of Gene-gene and Gene-environment InteractionInformation Measure of Gene-gene and Gene-environment InteractionMeasure of Interaction between Gene and Continuous EnvironmentStatistics for Testing Gene-gene and Gene-Environment Interaction for Qualitative Trait with Common VariantsRelative Risk and Odds-ration-based Statistics for Testing Interaction between Gene and Discrete EnvironmentDisequilibrium-based Statistics for Testing Gene-gene InteractionInformation-based Statistics for Testing Gene-Gene InteractionHaplotype-Odds Ratio and Tests for Gene-Gene InteractionMultiplicative Measure-based Statistics for Testing Interaction between Gene and Continuous EnvironmentInformation Measure-based Statistics for Testing Interaction between Gene and Continuous EnvironmentReal ExampleStatistics for Testing Gene-gene and Gene-Environment Interaction for Qualitative Trait with Next-generation Sequencing DataMultiple Logistic Regression Model for Gene-Gene Interaction AnalysisFunctional logistic regression model for gene-gene interaction analysisStatistics for Testing Interaction between Two Genomic RegionsStatistics for Testing Gene-gene and Gene-Environment Interaction for Quantitative TraitsGenetic Models for Epistasis Effects of Quantitative TraitsRegression Model for Interaction Analysis with Quantitative TraitsFunctional Regression Model for Interaction Analysis with a Quantitative TraitFunctional Regression Model for Interaction Analysis with Multiple Quantitative TraitsMultivariate and Functional Canonical Correlation as a Unified Framework for Testing Gen-Gene and Gene-Environment Interaction for both Qualitative and Quantitative TraitsData Structure of CCA for Interaction AnalysisCCA and Functional CCAKernel CCASoftware PackageAppendicesExerciseMachine Learning, Low Rank Models and Their Application to Disease Risk Prediction and Precision MedicineLogistic RegressionTwo Class Logistic RegressionMulticlass Logistic RegressionParameter EstimationTest StatisticsNetwork Penalized Two-class Logistic RegressionNetwork Penalized Multiclass Logistic RegressionFisher\'s Linear Discriminant AnalysisFisher\'s Linear Discriminant Analysis for Two ClassesMulti-class Fisher\'s Linear Discriminant AnalysisConnections between Linear Discriminant Analysis, Optimal Scoring and Canonical Correlation Analysis (CCA)Support Vector MachineIntroductionLinear Support Vector MachinesNonlinear SVMPenalized SVMsLow Rank ApproximationQuadratically Regularized PCAGeneralized RegularizationGeneralized Canonical Correlation Analysis (CCA)Quadratically Regularized Canonical Correlation AnalysisSparse Canonical Correlation AnalysisSparse Canonical Correlation Analysis via a Penalized Matrix DecompositionInverse Regression (IR) and Sufficient Dimension ReductionSufficient Dimension Reduction (SDR) and Sliced Inverse Regression (SIR)Sparse SDRSoftware PackageAppendicesExercises ã ã ã ã ã