Book 2016

Big Data Analytics in Genomics

Editors:

ISBN: 978-3-319-41278-8 (Print) 978-3-319-41279-5 (Online)

Table of contents (13 chapters)

  1. Front Matter

    Pages i-viii

  2. Statistical Analytics

    1. Front Matter

      Pages 1-1

    2. No Access

      Chapter

      Pages 3-23

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

    3. No Access

      Chapter

      Pages 25-88

      Robust Methods for Expression Quantitative Trait Loci Mapping

    4. No Access

      Chapter

      Pages 89-143

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

    5. No Access

      Chapter

      Pages 145-167

      Genomic Applications of the Neyman–Pearson Classification Paradigm

  3. Computational Analytics

    1. Front Matter

      Pages 169-169

    2. No Access

      Chapter

      Pages 171-195

      Improving Re-annotation of Annotated Eukaryotic Genomes

    3. No Access

      Chapter

      Pages 197-223

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

    4. No Access

      Chapter

      Pages 225-298

      A Survey of Computational Methods for Protein Function Prediction

    5. No Access

      Chapter

      Pages 299-313

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

  4. Cancer Analytics

    1. Front Matter

      Pages 315-315

    2. No Access

      Chapter

      Pages 317-336

      Perspectives of Machine Learning Techniques in Big Data Mining of Cancer

    3. No Access

      Chapter

      Pages 337-355

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

    4. No Access

      Chapter

      Pages 357-372

      NGS Analysis of Somatic Mutations in Cancer Genomes

    5. No Access

      Chapter

      Pages 373-396

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

    6. No Access

      Chapter

      Pages 397-428

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