Big Data Analytics in Genomics

 Previously published in hardcover
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ISBN-13:
9783319823126
Einband:
Previously published in hardcover
Erscheinungsdatum:
22.04.2018
Seiten:
436
Autor:
Ka-Chun Wong
Gewicht:
655 g
Format:
235x155x23 mm
Sprache:
Englisch
Beschreibung:

Treats both theoretical and practical aspects of scalable data analysis in genome researchCovers various applications in high impact problems, such as cancer genome analyticsIncludes concrete cases that illustrate how to develop solid computational pipelines for genomics
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.
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.

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