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Nonmonotonic Learning in Large Biological Networks

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Inductive Logic Programming

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9046))

Abstract

This paper introduces a new open-source implementation of a nonmonotonic learning method called XHAIL and shows how it can be used for abductive and inductive inference on metabolic networks that are many times larger than could be handled by the preceding prototype. We summarise several implementation improvements that increase its efficiency and we introduce an extended form of language bias that further increases its usability. We investigate the system’s scalability in a case study involving real data previously collected by a Robot Scientist and show how it led to the discovery of an error in a whole-organism model of yeast metabolism.

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Notes

  1. 1.

    https://github.com/cathexis-bris-ac-uk/XHAIL.

  2. 2.

    In fact this change was already present in the model used in [16] but the description in the paper incorrectly reproduced an earlier version of the rule from [14].

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Acknowledgments

This work is supported by EPSRC grant EP/K035959/1.

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Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Bristol, BS8 1TH, UK

    Stefano Bragaglia & Oliver Ray

Authors
  1. Stefano Bragaglia
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  2. Oliver Ray
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Correspondence to Oliver Ray .

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Editors and Affiliations

  1. Department of Computer Science, KU Leuven, Leuven, Belgium

    Jesse Davis

  2. Department of Computer Science, KU Leuven, Leuven, Belgium

    Jan Ramon

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Bragaglia, S., Ray, O. (2015). Nonmonotonic Learning in Large Biological Networks. In: Davis, J., Ramon, J. (eds) Inductive Logic Programming. Lecture Notes in Computer Science(), vol 9046. Springer, Cham. https://doi.org/10.1007/978-3-319-23708-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-23708-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23707-7

  • Online ISBN: 978-3-319-23708-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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