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Logic and the Automatic Acquisition of Scientific Knowledge: An Application to Functional Genomics

  • Ross D. King
  • Andreas Karwath
  • Amanda Clare
  • Luc Dehaspe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4660)

Abstract

This paper is a manifesto aimed at computer scientists interested in developing and applying scientific discovery methods. It argues that: science is experiencing an unprecedented “explosion” in the amount of available data; traditional data analysis methods cannot deal with this increased quantity of data; there is an urgent need to automate the process of refining scientific data into scientific knowledge; inductive logic programming (ILP) is a data analysis framework well suited for this task; and exciting new scientific discoveries can be achieved using ILP scientific discovery methods. We describe an example of using ILP to analyse a large and complex bioinformatic database that has produced unexpected and interesting scientific results in functional genomics. We then point a possible way forward to integrating machine learning with scientific databases to form intelligent databases.

Keywords

Logic Program Functional Genomic Scientific Discovery Inductive Logic Programming Automatic Acquisition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ross D. King
    • 1
  • Andreas Karwath
    • 2
  • Amanda Clare
    • 1
  • Luc Dehaspe
    • 3
  1. 1.Department of Computer Science, University of Wales, AberystwythU.K.
  2. 2.Albert-Ludwigs Universität, Institut für Informatik, Georges-Köhler-Allee 079, D-79110 FreiburgGermany
  3. 3.PharmaDM, HeverleeBelgium

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