Logic and the automatic acquisition of scientific knowledge: An application to functional genomics

Ross D. King, Andreas Karwath, Amanda Clare, Luc Dehaspe

    Research output: Chapter in Book/Report/Conference proceedingChapter

    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. © Springer-Verlag Berlin Heidelberg 2007.
    Original languageEnglish
    Title of host publicationComputational Discovery of Scientific Knowledge
    Subtitle of host publicationIntroduction, Techniques, and Applications in Environmental and Life Sciences
    EditorsSašo Džeroski, Ljupčo Todorovski
    PublisherSpringer Nature
    Pages273-289
    Number of pages16
    Volume4660
    ISBN (Print)9783540739197
    DOIs
    Publication statusPublished - 2007

    Publication series

    Name Lecture notes in computer science

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