Skip to main content
SpringerLink
Log in
Book cover

Big Data Analytics in Genomics

  • Book
  • © 2016

Overview

Editors:
  1. Ka-Chun Wong

    1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • 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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (13 chapters)

  1. Front Matter

    Pages i-viii
    Download chapter PDF

  2. Statistical Analytics

    1. Front Matter

      Pages 1-1
      Download chapter PDF

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

      • Can Yang, Xiang Wan, Jin Liu, Michael Ng
      Pages 3-23

    3. Robust Methods for Expression Quantitative Trait Loci Mapping

      • Wei Cheng, Xiang Zhang, Wei Wang
      Pages 25-88

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

      • Adèle H. Ribeiro, Júlia M. P. Soler, Elias Chaibub Neto, André Fujita
      Pages 89-143

    5. Genomic Applications of the Neyman–Pearson Classification Paradigm

      • Jingyi Jessica Li, Xin Tong
      Pages 145-167

  3. Computational Analytics

    1. Front Matter

      Pages 169-169
      Download chapter PDF

    2. Improving Re-annotation of Annotated Eukaryotic Genomes

      • Shishir K. Gupta, Elena Bencurova, Mugdha Srivastava, Pirasteh Pahlavan, Johannes Balkenhol, Thomas Dandekar
      Pages 171-195

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

      • Enzo Rucci, Carlos García, Guillermo Botella, Armando De Giusti, Marcelo Naiouf, Manuel Prieto-Matías
      Pages 197-223

    4. A Survey of Computational Methods for Protein Function Prediction

      • Amarda Shehu, Daniel Barbará, Kevin Molloy
      Pages 225-298

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

      • Muniyandi Nagarajan, Vandana R. Prabhu
      Pages 299-313

  4. Cancer Analytics

    1. Front Matter

      Pages 315-315
      Download chapter PDF

    2. Perspectives of Machine Learning Techniques in Big Data Mining of Cancer

      • Archana Prabahar, Subashini Swaminathan
      Pages 317-336

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

      • Pan Tong, Hua Li
      Pages 337-355

    4. NGS Analysis of Somatic Mutations in Cancer Genomes

      • T. Prieto, J. M. Alves, D. Posada
      Pages 357-372

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

      • Ming-Ying Leung, Joseph A. Knapka, Amy E. Wagler, Georgialina Rodriguez, Robert A. Kirken
      Pages 373-396

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

      • Sohiya Yotsukura, Masayuki Karasuyama, Ichigaku Takigawa, Hiroshi Mamitsuka
      Pages 397-428
Back to top

Keywords

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.




Reviews

“This edited volume is intended to showcase the current research on big data analytics for genomics … . The edited volume is well-organized, structured, and topics appeared sequentially. Most of the chapters are self-contained. … this is a good collection of work in one place; I think this volume will attract a broader audience. I enjoyed reading a few chapters of the book and found them interesting and useful.” (Technometrics, Vol. 59 (2), April, 2017)

Editors and Affiliations

  • Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

About the editor

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.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation