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Nonmonotonic Learning in Large Biological Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 proceedingConference contribution

Harvard

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

APA

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

Vancouver

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

Author

Ray, Oliver ; Bragaglia, Stefano. / Nonmonotonic Learning in Large Biological Networks. Inductive Logic Programming: 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers. editor / Jesse Davis ; Jan Ramon. 2015. pp. 33-48 (Lecture Notes in Computer Science).

Bibtex

@inproceedings{44a3defc000c4f4db098c729cb41bd07,
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’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",
author = "Oliver Ray and Stefano Bragaglia",
year = "2015",
month = "12",
day = "27",
doi = "10.1007/978-3-319-23708-4_3",
language = "English",
isbn = "9783319237077",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "33--48",
editor = "Jesse Davis and Jan Ramon",
booktitle = "Inductive Logic Programming",

}

RIS - suitable for import to EndNote

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T1 - Nonmonotonic Learning in Large Biological Networks

AU - Ray, Oliver

AU - Bragaglia, Stefano

PY - 2015/12/27

Y1 - 2015/12/27

N2 - 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.

AB - 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.

KW - ILP

KW - ALP

KW - ASP

KW - metabolic networks

KW - completion

KW - revision

U2 - 10.1007/978-3-319-23708-4_3

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

M3 - Conference contribution

SN - 9783319237077

T3 - Lecture Notes in Computer Science

SP - 33

EP - 48

BT - Inductive Logic Programming

A2 - Davis, Jesse

A2 - Ramon, Jan

ER -