Nonmonotonic Learning in Large Biological Networks
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.
KeywordsILP ALP ASP Metabolic networks Completion Revision
This work is supported by EPSRC grant EP/K035959/1.
- 2.Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: clasp: A conflict-driven answer set solver. In: Logic Programming and Nonmonotonic Reasoning, pp. 260–265. Springer (2007)Google Scholar
- 8.Lehninger, A.: Biochemistry: The Molecular Basis of Cell Structure and Function, 2nd edn. Worth Publishers, New York (1979)Google Scholar
- 13.Ray, O.: Hybrid Abductive-Inductive Learning. Ph.D. thesis, Department of Computing, Imperial College London, UK (2005)Google Scholar
- 14.Ray, O., Whelan, K., King, R.: A nonmonotonic logical approach for modelling and revising metabolic networks. In: Proceedings of 3rd International Conference on Complex, Intelligent and Software Intensive Systems, pp. 825–829. IEEE (2009)Google Scholar