Big Data Analytics in Genomics

Editors: Wong, Ka-Chun (Ed.)

  • Treats both theoretical and practical aspects of scalable data analysis in genome research
  • Covers various applications in high impact problems, such as cancer genome analytics
  • Includes concrete cases that illustrate how to develop solid computational pipelines for genomics
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eBook 91,62 €
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  • ISBN 978-3-319-41279-5
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Hardcover 114,39 €
price for Spain (gross)
  • ISBN 978-3-319-41278-8
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About this book

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.

About the authors

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.

Table of contents (13 chapters)

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

    Yang, Can (et al.)

    Pages 3-23

    Preview Buy Chapter 30,19 €
  • Robust Methods for Expression Quantitative Trait Loci Mapping

    Cheng, Wei (et al.)

    Pages 25-88

    Preview Buy Chapter 30,19 €
  • Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation

    Ribeiro, Adèle H. (et al.)

    Pages 89-143

    Preview Buy Chapter 30,19 €
  • Genomic Applications of the Neyman–Pearson Classification Paradigm

    Li, Jingyi Jessica (et al.)

    Pages 145-167

    Preview Buy Chapter 30,19 €
  • Improving Re-annotation of Annotated Eukaryotic Genomes

    Gupta, Shishir K. (et al.)

    Pages 171-195

    Preview Buy Chapter 30,19 €

Buy this book

eBook 91,62 €
price for Spain (gross)
  • ISBN 978-3-319-41279-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 114,39 €
price for Spain (gross)
  • ISBN 978-3-319-41278-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Big Data Analytics in Genomics
Editors
  • Ka-Chun Wong
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland (Outside the USA)
eBook ISBN
978-3-319-41279-5
DOI
10.1007/978-3-319-41279-5
Hardcover ISBN
978-3-319-41278-8
Edition Number
1
Number of Pages
VIII, 428
Number of Illustrations and Tables
12 b/w illustrations, 58 illustrations in colour
Topics