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|a Big data analytics in genomics /
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|a Introduction to Statistical Methods for Integrative Analysis of Genomic Data -- Robust Methods for Expression Quantitative Trait Loci Mapping -- Causal Inference and Structure Learning of Genotype-Phenotype Networks using Genetic Variation -- Genomic Applications of the Neyman-Pearson Classification Paradigm -- Improving Re-annotation of Annotated Eukaryotic Genomes -- State-of-the-art in Smith-Waterman Protein Database Search -- A Survey of Computational Methods for Protein Function Prediction -- Genome Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeast -- Perspectives of Machine Learning Techniques in Big Data Mining of Cancer -- Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms -- NGC Analysis of Somatic Mutations in Cancer Genomes -- OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancer -- A Bioinformatics Approach for Understanding Genotype-Phenotype Correlation in Breast Cancer.
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|a This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field. This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.
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| 650 |
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|a Big data.
|0 http://id.loc.gov/authorities/subjects/sh2012003227
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|a Genomics.
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|a Data mining.
|0 http://id.loc.gov/authorities/subjects/sh97002073
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|a Quantitative research
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|a Genomics.
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|a Electronic books.
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|a Wong, Ka-Chun,
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|i Print version:
|t Big Data Analytics in Genomics.
|d [Place of publication not identified] : Springer-Verlag New York Inc 2016
|z 9783319412788
|w (OCoLC)951761276
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| 880 |
8 |
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|6 505-00/(S
|a 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 -- 6 Causal Structure Learning -- 6.1 Learning Structural Equation Models -- 6.2 Learning Causal Graphical Models -- 6.2.1 Constraint-Based Approaches -- 6.2.2 Score-Based Approaches -- 7 Algorithms for Causal Discovery in Genetic Systems -- 7.1 QTL-Directed Dependency Graph Algorithm -- 7.2 QTL+Phenotype Supervised Orientation Algorithm -- 7.3 QTL-Driven Phenotype Network Algorithm -- 7.4 Sparsity-Aware Maximum Likelihood Algorithm -- 7.5 Summary -- 8 Conclusions -- References -- Genomic Applications of the Neyman-Pearson Classification Paradigm -- 1 Introduction -- 2 Neyman-Pearson Paradigm -- 2.1 An Estimate of Cα -- 2.2 The NP Umbrella Algorithm -- 3 Simulation -- 3.1 Logistic Regression -- 3.2 Support Vector Machines -- 3.3 Random Forests -- 4 Case Study.
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|a 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.
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