مشخصات کتاب

Big data in omics and imaging. Integrated analysis and causal inference

ویرایش:  
نویسندگان: Xiong. Momiao  
سری: Chapman and Hall/CRC mathematical & computational biology series 
ISBN (شابک) : 9781351172622, 1351172638 
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: 24 مگابایت 

کلمات کلیدی مربوط به کتاب داده های بزرگ در omics و تصویربرداری. تحلیل تلفیقی و استنتاج علی: بیومتری -- پردازش داده ها، سیستم های تصویربرداری در زیست شناسی -- روش های آماری، داده های بزرگ -- روش های آماری، سلامت و تناسب اندام / کل نگر، سلامت و تناسب اندام / مرجع، پزشکی / جایگزین، پزشکی / اطلس، پزشکی / مقاله، پزشکی / خانواده و عمل عمومی، پزشکی / کل نگر، پزشکی / استئوپاتی، بیوانفورماتیک، STATSnetBASE، SCI-TECHnetBASE، COMPUTERSCIENCEnetBASE، BIOMEDICALSCIENCEnetBASE، INFORMATIONSCIENCEnetBASE، STMnetBASE

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توجه داشته باشید کتاب داده های بزرگ در omics و تصویربرداری. تحلیل تلفیقی و استنتاج علی 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.

توضیحاتی درمورد کتاب به خارجی

"Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. ? FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks ? The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell -specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis."-- Read more...
Abstract: "Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. ? FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks ? The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell -specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis."

فهرست مطالب

Content: Cover
 Half Title
 Title Page
 Copyright Page
 Contents
 Preface
 Author
 Chapter 1: Genotype-Phenotype Network Analysis
 1.1 Undirected Graphs for Genotype Network
 1.1.1 Gaussian Graphic Model
 1.1.2 Alternating Direction Method of Multipliers for Estimation of Gaussian Graphical Model
 1.1.3 Coordinate Descent Algorithm and Graphical Lasso
 1.1.4 Multiple Graphical Models
 1.1.4.1 Edge-Based Joint Estimation of Multiple Graphical Models
 1.1.4.2 Node-Based Joint Estimation of Multiple Graphical Models
 1.2 Directed Graphs and Structural Equation Models for Networks 1.2.1 Directed Acyclic Graphs1.2.2 Linear Structural Equation Models
 1.2.3 Estimation Methods
 1.2.3.1 Maximum Likelihood (ML) Estimation
 1.2.3.2 Two-Stage Least Squares Method
 1.2.3.3 Three-Stage Least Squares Method
 1.3 Sparse Linear Structural Equations
 1.3.1 L1-Penalized Maximum Likelihood Estimation
 1.3.2 L1-Penalized Two Stage Least Square Estimation
 1.3.3 L1-Penalized Three-Stage Least Square Estimation
 1.4 Functional Structural Equation Models for Genotype-Phenotype Networks
 1.4.1 Functional Structural Equation Models 1.4.2 Group Lasso and ADMM for Parameter Estimation in the Functional Structural Equation Models1.5 Causal Calculus
 1.5.1 Effect Decomposition and Estimation
 1.5.2 Graphical Tools for Causal Inference in Linear SEMs
 1.5.2.1 Basics
 1.5.2.2 Wright's Rules of Tracing and Path Analysis
 1.5.2.3 Partial Correlation, Regression, and Path Analysis
 1.5.2.4 Conditional Independence and D-Separation
 1.5.3 Identification and Single-Door Criterion
 1.5.4 Instrument Variables
 1.5.5 Total Effects and Backdoor Criterion
 1.5.6 Counterfactuals and Linear SEMs
 1.6 Simulations and Real Data Analysis 1.6.1 Simulations for Model Evaluation1.6.2 Application to Real Data Examples
 Appendix 1.A
 Appendix 1.B
 Exercises
 Chapter 2: Causal Analysis and Network Biology
 2.1 Bayesian Networks as a General Framework for Causal Inference
 2.2 Parameter Estimation and Bayesian Dirichlet Equivalent Uniform Score for Discrete Bayesian Networks
 2.3 Structural Equations and Score Metrics for Continuous Causal Networks
 2.3.1 Multivariate SEMs for Generating Node Core Metrics
 2.3.2 Mixed SEMs for Pedigree-Based Causal Inference
 2.3.2.1 Mixed SEMs 2.3.2.2 Two-Stage Estimate for the Fixed Effects in the Mixed SEMs2.3.2.3 Three-Stage Estimate for the Fixed Effects in the Mixed SEMs
 2.3.2.4 The Full Information Maximum Likelihood Method
 2.3.2.5 Reduced Form Representation of the Mixed SEMs
 2.4 Bayesian Networks with Discrete and Continuous Variables
 2.4.1 Two-Class Network Penalized Logistic Regression for Learning Hybrid Bayesian Networks
 2.4.2 Multiple Network Penalized Functional Logistic Regression Models for NGS Data
 2.4.3 Multi-Class Network Penalized Logistic Regression for Learning Hybrid Bayesian Networks

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