Nonmonotonic Learning in Large Biological Networks

Oliver Ray, Stefano Bragaglia

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
285 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationInductive Logic Programming
Subtitle of host publication24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers
EditorsJesse Davis, Jan Ramon
Pages33-48
Number of pages16
ISBN (Electronic) 9783319237084
DOIs
Publication statusPublished - 27 Dec 2015
Event24th International Conference, ILP 2014 - Nancy, France
Duration: 14 Sep 201416 Sep 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9046
ISSN (Print)0302-9743
ISSN (Electronic)0302-9743

Conference

Conference24th International Conference, ILP 2014
CountryFrance
CityNancy
Period14/09/1416/09/14

Keywords

  • ILP
  • ALP
  • ASP
  • metabolic networks
  • completion
  • revision

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    Cite this

    Ray, O., & Bragaglia, S. (2015). Nonmonotonic Learning in Large Biological Networks. In J. Davis, & J. Ramon (Eds.), Inductive Logic Programming: 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers (pp. 33-48). (Lecture Notes in Computer Science; Vol. 9046). https://doi.org/10.1007/978-3-319-23708-4_3