Learning qualitative metabolic models

George MacLeod Coghill, S. M. Garrett, R. D. King

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

9 Citations (Scopus)

Abstract

The ability to learn a model of a system from observations of the system and background knowledge is central to intelligence, and the automation of the process is a key research goal of Artificial Intelligence. We present a model- learning system, developed for application to scientific discovery problems, where the models are scientific hypotheses and the observations are experiments. The learning system, QOPH learns the structural relationships between the observed variables, known to be a hard problem. QOPH has been shown capable of learning models with hidden (unmeasured) variables, under different levels of noise, and from qualitative or quantitative input data.

Original languageEnglish
Title of host publicationECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings
EditorsRamon Lopez de Mantaras, L. Saitta
Place of PublicationAmsterdam, Netherlands
PublisherIOS Press
Pages445-449
Number of pages5
ISBN (Print)1586034529, 978-1586034528
Publication statusPublished - Nov 2004
Event16th European conference on Artificial Intelligence - Valencia, Spain
Duration: 22 Aug 200427 Aug 2004

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Volume110
ISSN (Print)0922-6389

Conference

Conference16th European conference on Artificial Intelligence
CountrySpain
CityValencia
Period22/08/0427/08/04

Cite this

Coghill, G. M., Garrett, S. M., & King, R. D. (2004). Learning qualitative metabolic models. In R. Lopez de Mantaras, & L. Saitta (Eds.), ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings (pp. 445-449). (Frontiers in Artificial Intelligence and Applications; Vol. 110). Amsterdam, Netherlands: IOS Press.

Learning qualitative metabolic models. / Coghill, George MacLeod; Garrett, S. M.; King, R. D.

ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings. ed. / Ramon Lopez de Mantaras; L. Saitta. Amsterdam, Netherlands : IOS Press, 2004. p. 445-449 (Frontiers in Artificial Intelligence and Applications; Vol. 110).

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

Coghill, GM, Garrett, SM & King, RD 2004, Learning qualitative metabolic models. in R Lopez de Mantaras & L Saitta (eds), ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings. Frontiers in Artificial Intelligence and Applications, vol. 110, IOS Press, Amsterdam, Netherlands, pp. 445-449, 16th European conference on Artificial Intelligence, Valencia, Spain, 22/08/04.
Coghill GM, Garrett SM, King RD. Learning qualitative metabolic models. In Lopez de Mantaras R, Saitta L, editors, ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings. Amsterdam, Netherlands: IOS Press. 2004. p. 445-449. (Frontiers in Artificial Intelligence and Applications).
Coghill, George MacLeod ; Garrett, S. M. ; King, R. D. / Learning qualitative metabolic models. ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings. editor / Ramon Lopez de Mantaras ; L. Saitta. Amsterdam, Netherlands : IOS Press, 2004. pp. 445-449 (Frontiers in Artificial Intelligence and Applications).
@inproceedings{375947a45a224b778a3a212e1ed1a0f6,
title = "Learning qualitative metabolic models",
abstract = "The ability to learn a model of a system from observations of the system and background knowledge is central to intelligence, and the automation of the process is a key research goal of Artificial Intelligence. We present a model- learning system, developed for application to scientific discovery problems, where the models are scientific hypotheses and the observations are experiments. The learning system, QOPH learns the structural relationships between the observed variables, known to be a hard problem. QOPH has been shown capable of learning models with hidden (unmeasured) variables, under different levels of noise, and from qualitative or quantitative input data.",
author = "Coghill, {George MacLeod} and Garrett, {S. M.} and King, {R. D.}",
year = "2004",
month = "11",
language = "English",
isbn = "1586034529",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "445--449",
editor = "{Lopez de Mantaras}, Ramon and L. Saitta",
booktitle = "ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings",

}

TY - GEN

T1 - Learning qualitative metabolic models

AU - Coghill, George MacLeod

AU - Garrett, S. M.

AU - King, R. D.

PY - 2004/11

Y1 - 2004/11

N2 - The ability to learn a model of a system from observations of the system and background knowledge is central to intelligence, and the automation of the process is a key research goal of Artificial Intelligence. We present a model- learning system, developed for application to scientific discovery problems, where the models are scientific hypotheses and the observations are experiments. The learning system, QOPH learns the structural relationships between the observed variables, known to be a hard problem. QOPH has been shown capable of learning models with hidden (unmeasured) variables, under different levels of noise, and from qualitative or quantitative input data.

AB - The ability to learn a model of a system from observations of the system and background knowledge is central to intelligence, and the automation of the process is a key research goal of Artificial Intelligence. We present a model- learning system, developed for application to scientific discovery problems, where the models are scientific hypotheses and the observations are experiments. The learning system, QOPH learns the structural relationships between the observed variables, known to be a hard problem. QOPH has been shown capable of learning models with hidden (unmeasured) variables, under different levels of noise, and from qualitative or quantitative input data.

M3 - Conference contribution

SN - 1586034529

SN - 978-1586034528

T3 - Frontiers in Artificial Intelligence and Applications

SP - 445

EP - 449

BT - ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings

A2 - Lopez de Mantaras, Ramon

A2 - Saitta, L.

PB - IOS Press

CY - Amsterdam, Netherlands

ER -