© 2007

Computational Discovery of Scientific Knowledge

Introduction, Techniques, and Applications in Environmental and Life Sciences

  • Editors
  • Sašo Džeroski
  • Ljupčo Todorovski

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4660)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4660)

Table of contents

  1. Front Matter
  2. Computational Discovery of Scientific Knowledge

    1. Sašo Džeroski, Pat Langley, Ljupčo Todorovski
      Pages 1-14
  3. I Equation Discovery and Dynamic Systems Identification

    1. Front Matter
      Pages 15-15
    2. Reinhard Stolle, Elizabeth Bradley
      Pages 17-43
    3. Matthew Easley, Elizabeth Bradley
      Pages 44-68
    4. Ljupčo Todorovski, Sašo Džeroski
      Pages 69-97
    5. Takashi Washio, Hiroshi Motoda
      Pages 98-119
    6. Kazumi Saito, Pat Langley
      Pages 120-137
    7. Mark Schwabacher, Pat Langley, Christopher Potter, Steven Klooster, Alicia Torregrosa
      Pages 138-157
    8. Feng Zhao, Chris Bailey-Kellogg, Xingang Huang, Iván Ordóñez
      Pages 158-174
    9. Simon Colton
      Pages 175-201
  4. II Computational Scientific Discovery in Biomedicine

    1. Front Matter
      Pages 203-203
    2. John R. Koza, William Mydlowec, Guido Lanza, Jessen Yu, Martin A. Keane
      Pages 205-227
    3. Blaž Zupan, Ivan Bratko, Janez Demšar, Peter Juvan, Adam Kuspa, John A. Halter et al.
      Pages 228-247
    4. Simon M. Garrett, George M. Coghill, Ashwin Srinivasan, Ross D. King
      Pages 248-272
    5. Ross D. King, Andreas Karwath, Amanda Clare, Luc Dehaspe
      Pages 273-289
    6. Dimitar Hristovski, Borut Peterlin, Sašo Džeroski, Janez Stare
      Pages 307-326
  5. Back Matter

About this book


Advances in technology have enabled the collection of data from scientific observations, simulations, and experiments at an ever-increasing pace. For the scientist and engineer to benefit from these enhanced data collecting capabilities, it is becoming clear that semi-automated data analysis techniques must be applied to find the useful information in the data. Computational scientific discovery methods can be used to this end: they focus on applying computational methods to automate scientific activities, such as finding laws from observational data. In contrast to mining scientific data, which focuses on building highly predictive models, computational scientific discovery puts a strong emphasis on discovering knowledge represented in formalisms used by scientists and engineers, such as numeric equations and reaction pathways.

This state-of-the-art survey provides an introduction to computational approaches to the discovery of scientific knowledge and gives an overview of recent advances in this area, including techniques and applications in environmental and life sciences. The 15 articles presented are partly inspired by the contributions of the International Symposium on Computational Discovery of Communicable Knowledge, held in Stanford, CA, USA in March 2001. More representative coverage of recent research in computational scientific discovery is achieved by a significant number of additional invited contributions.


DOM Simulation automated modeling bioinformatics biomedical applications constraint-based mining data mining decision trees equation discovery genet genetic pathways genetic programming genome life sciences programming

Bibliographic information