Data Mining Techniques for the Life Sciences

  • Oliviero Carugo
  • Frank Eisenhaber

Part of the Methods in Molecular Biology book series (MIMB, volume 609)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Databases

    1. Front Matter
      Pages 1-1
    2. Stefan Washietl, Ivo L. Hofacker
      Pages 3-15
    3. Michael Rebhan
      Pages 45-57
    4. Roman A. Laskowski
      Pages 59-82
    5. Nicola J. Mulder
      Pages 83-95
    6. M. Michael Gromiha, Akinori Sarai
      Pages 97-112
    7. Dietmar Schomburg, Ida Schomburg
      Pages 113-128
    8. Hong Sain Ooi, Georg Schneider, Teng-Ting Lim, Ying-Leong Chan, Birgit Eisenhaber, Frank Eisenhaber
      Pages 129-144
    9. Hong Sain Ooi, Georg Schneider, Ying-Leong Chan, Teng-Ting Lim, Birgit Eisenhaber, Frank Eisenhaber
      Pages 145-159
  3. Data Mining Techniques

    1. Front Matter
      Pages 161-161
    2. Oliviero Carugo
      Pages 163-174
    3. Oliviero Carugo
      Pages 175-196
    4. Zheng Rong Yang
      Pages 197-222
    5. Asa Ben-Hur, Jason Weston
      Pages 223-239
    6. Claus Vogl, Andreas Futschik
      Pages 241-253
  4. Database Annotations and Predictions

    1. Front Matter
      Pages 255-255
    2. Georg Schneider, Michael Wildpaner, Fernanda L. Sirota, Sebastian Maurer-Stroh, Birgit Eisenhaber, Frank Eisenhaber
      Pages 257-267
    3. Ernesto Picardi, Graziano Pesole
      Pages 269-284

About this book

Introduction

Whereas getting exact data about living systems and sophisticated experimental procedures have primarily absorbed the minds of researchers previously, the development of high-throughput technologies has caused the weight to increasingly shift to the problem of interpreting accumulated data in terms of biological function and biomolecular mechanisms. In Data Mining Techniques for the Life Sciences, experts in the field contribute valuable information about the sources of information and the techniques used for "mining" new insights out of databases. Beginning with a section covering the concepts and structures of important groups of databases for biomolecular mechanism research, the book then continues with sections on formal methods for analyzing biomolecular data and reviews of concepts for analyzing biomolecular sequence data in context with other experimental results that can be mapped onto genomes. As a volume of the highly successful Methods in Molecular Biology™ series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results.

Authoritative and easy to reference, Data Mining Techniques for the Life Sciences seeks to aid students and researchers in the life sciences who wish to get a condensed introduction into the vital world of biological databases and their many applications.

Keywords

Biological research Biomolecular data Clustering Computer-assisted data analysis Hidden Markov Model High-throughput technologies In silico Markov model National Center for Biotechnology Information algorithms cluster analysis data mining databases genome life sciences

Editors and affiliations

  • Oliviero Carugo
    • 1
  • Frank Eisenhaber
    • 2
  1. 1.Max F. Perutz Laboratories GmbHUniversität WienWienAustria
  2. 2.Research (A*STAR)Agency for Science & TechnologySingaporeSingapore

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-60327-241-4
  • Copyright Information Humana Press 2010
  • Publisher Name Humana Press
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-60327-240-7
  • Online ISBN 978-1-60327-241-4
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • About this book