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Big Data Analytics in Genomics.

By: Wong, Ka-Chun.
Material type: TextTextSeries: eBooks on Demand.Publisher: Cham : Springer International Publishing, 2016Description: 1 online resource (426 p.).ISBN: 9783319412795.Subject(s): Big dataGenre/Form: Electronic books.Additional physical formats: Print version:: Big Data Analytics in GenomicsDDC classification: 004 LOC classification: QA75.5-76.95Online resources: Click here to view this ebook.
Contents:
Preface -- Contents -- Part I Statistical Analytics -- Introduction to Statistical Methods for Integrative Data Analysis in Genome-Wide Association Studies -- 1 Introduction -- 2 Heritability Estimation -- 2.1 The Basic Idea of Heritability Estimation from Pedigree Data -- 2.2 Heritability Estimation Based on GWAS -- 3 Integrative Analysis of Multiple GWAS -- 4 Integrative Analysis of GWAS with Functional Information -- 5 Case Study -- 6 Future Directions and Conclusion -- References -- Robust Methods for Expression Quantitative Trait Loci Mapping -- 1 Introduction -- 2 eQTL Mapping
2.1 Group-Wise eQTL Mapping and Challenges -- 2.2 Overview of the Developed Algorithms -- 2.3 Chapter Outline -- 3 Group-Wise eQTL Mapping -- 3.1 Introduction -- 3.2 Related Work -- 3.3 The Problem -- 3.4 Detecting Group-Wise Associations -- 3.4.1 SET-eQTL Model -- 3.4.2 Objective Function -- 3.5 Considering Confounding Factors -- 3.6 Incorporating Individual Effect -- 3.6.1 Objective Function -- 3.6.2 Increasing Computational Speed -- Updating σ2 -- Efficiently Inverting the Covariance Matrix -- 3.7 Optimization -- 3.8 Experimental Results -- 3.8.1 Simulation Study -- Shrinkage of C and BA
Computational Efficiency Evaluation -- 3.8.2 Yeast eQTL Study -- cis- and trans-Enrichment Analysis -- Reproducibility of trans Regulatory Hotspots Between Studies -- Gene Ontology Enrichment Analysis -- 3.9 Conclusion -- 4 Incorporating Prior Knowledge for Robust eQTL Mapping -- 4.1 Introduction -- 4.2 Background: Linear Regression with Graph Regularizer -- 4.2.1 Lasso and LORS -- 4.2.2 Graph-Regularized Lasso -- 4.3 Graph-Regularized Dual Lasso -- 4.3.1 Optimization: An Alternating Minimization Approach -- 4.3.2 Convergence Analysis -- 4.4 Generalized Graph-Regularized Dual Lasso
4.5 Experimental Results -- 4.5.1 Simulation Study -- 4.5.2 Yeast eQTL Study -- 4.5.3 cis and trans Enrichment Analysis -- 4.5.4 Refinement of the Prior Networks -- Hotspots Analysis -- 4.6 Conclusion -- 5 Discussion -- 5.1 Summary -- 5.2 Future Directions -- References -- Causal Inference and Structure Learning of Genotype-Phenotype Networks Using Genetic Variation -- 1 Introduction -- 2 Mendelian Randomization -- 2.1 Randomized Controlled Trial -- 2.2 Randomized Allocation of Allelic Variation in Genes -- 2.3 Genetic Variants as Instrumental Variables
2.3.1 Statistical Association with the Exposure -- 2.3.2 Independence with Exposure-Outcome Confounders -- 2.3.3 Exclusion Restriction -- 3 Causal Model -- 3.1 Functional Causal Representation -- 3.2 Graphical Causal Representation -- 4 Properties Relating Functional and Graphical Models -- 4.1 d-Separability -- 4.2 Global Directed Markov Property -- 4.2.1 Local Directed Markov Property in DAGs -- 4.3 Causal Faithfulness -- 4.4 Factorization of Joint Probability Distribution Functions -- 4.4.1 Factorization and Global Markov Property -- 4.5 Linear Entailment and Partial Correlations
5 Equivalent Models
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Preface -- Contents -- Part I Statistical Analytics -- Introduction to Statistical Methods for Integrative Data Analysis in Genome-Wide Association Studies -- 1 Introduction -- 2 Heritability Estimation -- 2.1 The Basic Idea of Heritability Estimation from Pedigree Data -- 2.2 Heritability Estimation Based on GWAS -- 3 Integrative Analysis of Multiple GWAS -- 4 Integrative Analysis of GWAS with Functional Information -- 5 Case Study -- 6 Future Directions and Conclusion -- References -- Robust Methods for Expression Quantitative Trait Loci Mapping -- 1 Introduction -- 2 eQTL Mapping

2.1 Group-Wise eQTL Mapping and Challenges -- 2.2 Overview of the Developed Algorithms -- 2.3 Chapter Outline -- 3 Group-Wise eQTL Mapping -- 3.1 Introduction -- 3.2 Related Work -- 3.3 The Problem -- 3.4 Detecting Group-Wise Associations -- 3.4.1 SET-eQTL Model -- 3.4.2 Objective Function -- 3.5 Considering Confounding Factors -- 3.6 Incorporating Individual Effect -- 3.6.1 Objective Function -- 3.6.2 Increasing Computational Speed -- Updating σ2 -- Efficiently Inverting the Covariance Matrix -- 3.7 Optimization -- 3.8 Experimental Results -- 3.8.1 Simulation Study -- Shrinkage of C and BA

Computational Efficiency Evaluation -- 3.8.2 Yeast eQTL Study -- cis- and trans-Enrichment Analysis -- Reproducibility of trans Regulatory Hotspots Between Studies -- Gene Ontology Enrichment Analysis -- 3.9 Conclusion -- 4 Incorporating Prior Knowledge for Robust eQTL Mapping -- 4.1 Introduction -- 4.2 Background: Linear Regression with Graph Regularizer -- 4.2.1 Lasso and LORS -- 4.2.2 Graph-Regularized Lasso -- 4.3 Graph-Regularized Dual Lasso -- 4.3.1 Optimization: An Alternating Minimization Approach -- 4.3.2 Convergence Analysis -- 4.4 Generalized Graph-Regularized Dual Lasso

4.5 Experimental Results -- 4.5.1 Simulation Study -- 4.5.2 Yeast eQTL Study -- 4.5.3 cis and trans Enrichment Analysis -- 4.5.4 Refinement of the Prior Networks -- Hotspots Analysis -- 4.6 Conclusion -- 5 Discussion -- 5.1 Summary -- 5.2 Future Directions -- References -- Causal Inference and Structure Learning of Genotype-Phenotype Networks Using Genetic Variation -- 1 Introduction -- 2 Mendelian Randomization -- 2.1 Randomized Controlled Trial -- 2.2 Randomized Allocation of Allelic Variation in Genes -- 2.3 Genetic Variants as Instrumental Variables

2.3.1 Statistical Association with the Exposure -- 2.3.2 Independence with Exposure-Outcome Confounders -- 2.3.3 Exclusion Restriction -- 3 Causal Model -- 3.1 Functional Causal Representation -- 3.2 Graphical Causal Representation -- 4 Properties Relating Functional and Graphical Models -- 4.1 d-Separability -- 4.2 Global Directed Markov Property -- 4.2.1 Local Directed Markov Property in DAGs -- 4.3 Causal Faithfulness -- 4.4 Factorization of Joint Probability Distribution Functions -- 4.4.1 Factorization and Global Markov Property -- 4.5 Linear Entailment and Partial Correlations

5 Equivalent Models

Description based upon print version of record.

Author notes provided by Syndetics

Ka-Chun Wong is Assistant Professor in the Department of Computer Science at City University of Hong Kong. He received his B.Eng. in Computer Engineering in 2008 and his M.Phil. degree in the Department of Computer Science and Engineering in 2010, both from United College, the Chinese University of Hong Kong. He finished his PhD at the Department of Computer Science at University of Toronto . His research interests include computational biology, bioinformatics, evolutionary computation, big data analytics, application machine learning, and interdisciplinary research.

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