Table of contents

  1. Front Matter
    Pages i-viii
  2. Statistical Analytics

    1. Front Matter
      Pages 1-1
    2. Wei Cheng, Xiang Zhang, Wei Wang
      Pages 25-88
    3. Adèle H. Ribeiro, Júlia M. P. Soler, Elias Chaibub Neto, André Fujita
      Pages 89-143
  3. Computational Analytics

    1. Front Matter
      Pages 169-169
    2. Shishir K. Gupta, Elena Bencurova, Mugdha Srivastava, Pirasteh Pahlavan, Johannes Balkenhol, Thomas Dandekar
      Pages 171-195
    3. Enzo Rucci, Carlos García, Guillermo Botella, Armando De Giusti, Marcelo Naiouf, Manuel Prieto-Matías
      Pages 197-223
    4. Amarda Shehu, Daniel Barbará, Kevin Molloy
      Pages 225-298
    5. Muniyandi Nagarajan, Vandana R. Prabhu
      Pages 299-313
  4. Cancer Analytics

    1. Front Matter
      Pages 315-315
    2. Archana Prabahar, Subashini Swaminathan
      Pages 317-336
    3. T. Prieto, J. M. Alves, D. Posada
      Pages 357-372
    4. Ming-Ying Leung, Joseph A. Knapka, Amy E. Wagler, Georgialina Rodriguez, Robert A. Kirken
      Pages 373-396
    5. Sohiya Yotsukura, Masayuki Karasuyama, Ichigaku Takigawa, Hiroshi Mamitsuka
      Pages 397-428

About this book

Introduction

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.


Keywords

Big Data Genomics Data Mining Genome Research Biostatistics Molecular Genetics Data Analytics Biology Bioinformatics Computational Biology Cancer Research Cancer Genomes Computational Science Scientific Computing Computational Annotation

Editors and affiliations

  • Ka-Chun Wong
    • 1
  1. 1.Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-41279-5
  • Copyright Information Springer International Publishing Switzerland (Outside the USA) 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-41278-8
  • Online ISBN 978-3-319-41279-5
  • About this book