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Big Data Analytics in Genomics

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Cover of 'Big Data Analytics in Genomics'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Statistical Methods for Integrative Data Analysis in Genome-Wide Association Studies
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    Chapter 2 Robust Methods for Expression Quantitative Trait Loci Mapping
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    Chapter 3 Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation
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    Chapter 4 Genomic Applications of the Neyman–Pearson Classification Paradigm
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    Chapter 5 Improving Re-annotation of Annotated Eukaryotic Genomes
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    Chapter 6 State-of-the-Art in Smith–Waterman Protein Database Search on HPC Platforms
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    Chapter 7 A Survey of Computational Methods for Protein Function Prediction
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    Chapter 8 Genome-Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeast
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    Chapter 9 Perspectives of Machine Learning Techniques in Big Data Mining of Cancer
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    Chapter 10 Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms
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    Chapter 11 NGS Analysis of Somatic Mutations in Cancer Genomes
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    Chapter 12 OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancer
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    Chapter 13 A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancer
Attention for Chapter 3: Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation
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