Nonmonotonic Learning in Large Biological Networks

Oliver Ray; Stefano Bragaglia

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

Nonmonotonic Learning in Large Biological Networks

Oliver Ray, Stefano Bragaglia

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

2 Citations (Scopus)

262 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 language	English
Title of host publication	Inductive Logic Programming
Subtitle of host publication	24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers
Editors	Jesse Davis, Jan Ramon
Pages	33-48
Number of pages	16
ISBN (Electronic)	9783319237084
DOIs	https://doi.org/10.1007/978-3-319-23708-4_3
Publication status	Published - 27 Dec 2015
Event	24th International Conference, ILP 2014 - Nancy, France Duration: 14 Sep 2014 → 16 Sep 2014

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9046
ISSN (Print)	0302-9743
ISSN (Electronic)	0302-9743

Conference

Conference	24th International Conference, ILP 2014
Country	France
City	Nancy
Period	14/09/14 → 16/09/14

Keywords

ILP
ALP
ASP
metabolic networks
completion
revision

Access to Document

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

Oliver Ray - Nonmonotonic Learning in Large Biological Networks
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Springer at 10.1007/978-3-319-23708-4_3.
Accepted author manuscript, 1.51 MB

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Projects

1 Finished

Symbolic support for scientific reasoning in systems biology

Ray, O.

31/01/14 → 30/04/15

Project: Research

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

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title = "Nonmonotonic Learning in Large Biological Networks",

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{\textquoteright}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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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. Lecture Notes in Computer Science, vol. 9046, pp. 33-48, 24th International Conference, ILP 2014, Nancy, France, 14/09/14. https://doi.org/10.1007/978-3-319-23708-4_3

Nonmonotonic Learning in Large Biological Networks. / Ray, Oliver; Bragaglia, Stefano.

Inductive Logic Programming: 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers. ed. / Jesse Davis; Jan Ramon. 2015. p. 33-48 (Lecture Notes in Computer Science; Vol. 9046).

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

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