Big Data Analytics in Genomics

1. Auflage 2016
ISBN: 978-3-319-41278-8
Verlag: Springer International Publishing

Buch, Englisch, 428 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 7745 g

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.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA.  In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science.  Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.

Zielgruppe

Research

Autoren/Hrsg.

Wong, Ka-Chun

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.

Über Autor(innen)

Ka-Chun Wong is Assistant Professor in the Department of Computer Science at City University of Hong Kong. He received his B.Eng. in Computer Engineering in 2008 and his M.Phil. degree in the Department of Computer Science and Engineering in 2010, both from United College, the Chinese University of Hong Kong. He finished his PhD at the Department of Computer Science at University of Toronto. His research interests include computational biology, bioinformatics, evolutionary computation, big data analytics, application machine learning, and interdisciplinary research.