عنوان

Big data in omics and imaging.dollar5Integrated analysis and causal inference /

شابک

135117262X

شابک

1351172638

شابک

1351172646

شابک

9781351172622

شابک

9781351172639

شابک

9781351172646

شابک اشتباه

9780815387107

عنوان اصلي

Big data in omics and imaging.dollar5Integrated analysis and causal inference /

نام عام مواد

[Book]

نام نخستين پديدآور

Momiao Xiong.

محل نشرو پخش و غیره

Boca Raton, FL :

نام ناشر، پخش کننده و غيره

CRC Press, Taylor & Francis Group,

تاریخ نشرو بخش و غیره

[2018]

نام خاص و کميت اثر

1 online resource (xxix, 736 pages)

عنوان فروست

Chapman and Hall/CRC mathematical and computational biology series

متن يادداشت

Includes bibliographical references and index.

متن يادداشت

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.

متن يادداشت

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.

متن يادداشت

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.

متن يادداشت

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.

متن يادداشت

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.

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

"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."--

شماره استاندارد بين المللي کتاب و موسيقي

9781351172622

عنوان اصلي به زبان ديگر

Integrated analysis and causal inference

موضوع مستند نشده

Big data-- Statistical methods.

موضوع مستند نشده

Biometry-- Data processing.

موضوع مستند نشده

Imaging systems in biology-- Statistical methods.

موضوع مستند نشده

Bioinformatics.

موضوع مستند نشده

HEALTH & FITNESS-- Holism.

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HEALTH & FITNESS-- Reference.

موضوع مستند نشده

MEDICAL-- Alternative Medicine.

موضوع مستند نشده

MEDICAL-- Atlases.

موضوع مستند نشده

MEDICAL-- Essays.

موضوع مستند نشده

MEDICAL-- Family & General Practice.

موضوع مستند نشده

MEDICAL-- Holistic Medicine.

موضوع مستند نشده

MEDICAL-- Osteopathy.

موضوع مستند نشده

HEA-- 012000

موضوع مستند نشده

HEA-- 020000

موضوع مستند نشده

MED-- 004000

موضوع مستند نشده

MED-- 029000

موضوع مستند نشده

MED-- 040000

موضوع مستند نشده

MED-- 092000

موضوع مستند نشده

MED-- 101000

موضوع مستند نشده

MED-- 109000

موضوع مستند نشده

PS

شماره

شماره

ويراست

ويراست

شماره رده

شماره رده

نشانه اثر

نشانه اثر

شماره رده

مستند نام اشخاص تاييد نشده

Xiong, Momiao

تاريخ عمليات

20200822175338.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y