Big Data Analytics in Genomics

• Corporate Author: SpringerLink (Online service)
• Other Authors: Wong, Ka-Chun (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Subjects: Computer science.; Data mining.; Bioinformatics.; Biomathematics.; Statistics.; Computer Science.; Computational Biology/Bioinformatics.; Data Mining and Knowledge Discovery.; Statistics for Life Sciences, Medicine, Health Sciences.; Genetics and Population Dynamics.

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 a...

Table of Contents:

• 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.