© 2004

Artificial Intelligence Methods And Tools For Systems Biology

  • Editors
  • Werner Dubitzky
  • Francisco Azuaje

Part of the Computational Biology book series (COBO, volume 5)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Sally McClean, Bryan Scotney, Steve Robinson
    Pages 37-50
  3. Rajmund L. Somorjai, Murray E. Alexander, Richard Baumgartner, Stephanie Booth, Christopher Bowman, Aleksander Demko et al.
    Pages 67-85
  4. Robin Gras, David Hernandez, Patricia Hernandez, Nadine Zangger, Yoan Mescam, Julien Frey Olivier Martin et al.
    Pages 87-106
  5. Panos Dafas, Alexander Kozlenkov, Alan Robinson, Michael Schroeder
    Pages 107-127
  6. Patrick Lambrix
    Pages 129-145
  7. K. Bretonnel Cohen, Lawrence Hunter
    Pages 147-173
  8. Martin Stetter, Bernd Schürmann, Mathäus Dejori
    Pages 175-195
  9. Marco Loh, Miruna Szabo, Rita Almeida, Martin Stetter, Gustavo Deco
    Pages 197-215
  10. Back Matter
    Pages 217-221

About this book


This book provides simultaneously a design blueprint, user guide, research agenda, and communication platform for current and future developments in artificial intelligence (AI) approaches to systems biology. It places an emphasis on the molecular dimension of life phenomena and in one chapter on anatomical and functional modeling of the brain.

As design blueprint, the book is intended for scientists and other professionals tasked with developing and using AI technologies in the context of life sciences research. As a user guide, this volume addresses the requirements of researchers to gain a basic understanding of key AI methodologies for life sciences research. Its emphasis is not on an intricate mathematical treatment of the presented AI methodologies. Instead, it aims at providing the users with a clear understanding and practical know-how of the methods. As a research agenda, the book is intended for computer and life science students, teachers, researchers, and managers who want to understand the state of the art of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. Our aim was to maintain the readability and accessibility of a textbook throughout the chapters, rather than compiling a mere reference manual. The book is also intended as a communication platform seeking to bride the cultural and technological gap among key systems biology disciplines. To support this function, contributors have adopted a terminology and approach that appeal to audiences from different backgrounds.


Lazy learning Racter artificial intelligence bioinformatics classification genetic algorithm heuristics hidden Markov model knowledge learning life sciences machine learning metaheuristic modeling ontology

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