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Data Mining for Systems Biology

Methods and Protocols

  • Hiroshi Mamitsuka

Part of the Methods in Molecular Biology book series (MIMB, volume 1807)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Tommi Mäklin, Jukka Corander, Antti Honkela
    Pages 1-7
  3. Kévin Vervier, Pierre Mahé, Jean-Philippe Vert
    Pages 9-20
  4. Tarmo Äijö, Richard Bonneau, Harri Lähdesmäki
    Pages 37-50
  5. Tobias Frisch, Jonatan Gøttcke, Richard Röttger, Qihua Tan, Jan Baumbach
    Pages 51-62
  6. Stefano Perna, Arif Canakoglu, Pietro Pinoli, Stefano Ceri, Limsoon Wong
    Pages 63-81
  7. Kei-ichiro Takahashi, David A. duVerle, Sohiya Yotsukura, Ichigaku Takigawa, Hiroshi Mamitsuka
    Pages 95-111
  8. Ziyun Ding, Qing Wei, Daisuke Kihara
    Pages 113-130
  9. Jieyao Deng, Qingjun Yuan, Hiroshi Mamitsuka, Shanfeng Zhu
    Pages 195-202
  10. Shengwen Peng, Hiroshi Mamitsuka, Shanfeng Zhu
    Pages 203-209
  11. Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao
    Pages 211-224
  12. Back Matter
    Pages 241-243

About this book

Introduction

This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. 

Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency.

Keywords

Metagenomics Epigenomics Metabolomics Data sciences Machine learning Pharmaceutical science Artificial intelligence

Editors and affiliations

  • Hiroshi Mamitsuka
    • 1
  1. 1.Bioinformatics CenterKyoto UniversityUjiJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-8561-6
  • Copyright Information Springer Science+Business Media, LLC, part of Springer Nature 2018
  • Publisher Name Humana Press, New York, NY
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-4939-8560-9
  • Online ISBN 978-1-4939-8561-6
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • Buy this book on publisher's site