Nonmonotonic Learning in Large Biological Networks

Conference paper

DOI: 10.1007/978-3-319-23708-4_3

Volume 9046 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
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

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.

Keywords

ILP ALP ASP Metabolic networks Completion Revision 

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of BristolBristolUK