Big Data Analytics in Genomics

Chapter 1 Introduction to Statistical Methods for Integrative Data Analysis in Genome-Wide Association Studies

Chapter 3 Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation

Chapter 4 Genomic Applications of the Neyman–Pearson Classification Paradigm

Chapter 5 Improving Re-annotation of Annotated Eukaryotic Genomes

Chapter 6 State-of-the-Art in Smith–Waterman Protein Database Search on HPC Platforms

Chapter 7 A Survey of Computational Methods for Protein Function Prediction

Chapter 8 Genome-Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeast

Chapter 9 Perspectives of Machine Learning Techniques in Big Data Mining of Cancer

Chapter 10 Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms

Chapter 11 NGS Analysis of Somatic Mutations in Cancer Genomes

Chapter 12 OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancer

Chapter 13 A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancer