Learning Qualitative Metabolic Models Using Evolutionary Methods

Wei Pang; George MacLeod Coghill

doi:10.1109/FCST.2010.57

Learning Qualitative Metabolic Models Using Evolutionary Methods

Wei Pang, George MacLeod Coghill

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

3 Citations (Scopus)

Abstract

In this paper, an Evolutionary Qualitative Model Learning Framework (EQML) is proposed and tested by learning the qualitative metabolic models under the condition of incomplete knowledge. JMorven, a fuzzy qualitative reasoning engine, is slightly modified and integrated into the framework as a sub-module to represent and verify the candidate models. Three metabolic compartment models are tested by two evolutionary algorithms (Genetic Algorithm and Clonal Selection Algorithm) in EQML. Finally the efficiency of these two algorithms is evaluated.

Original language	English
Title of host publication	2010 Fifth International Conference on Frontier of Computer Science and Technology
Place of Publication	Changchun, Jilin Province
Publisher	IEEE Computer Society
Pages	436-441
Number of pages	6
ISBN (Print)	978-1-4244-7779-1
DOIs	https://doi.org/10.1109/FCST.2010.57
Publication status	Published - 2010
Event	Fifth International Conference on Frontier of Computer Science and Technology (FCST), 2010 - Changchun, Jilin Province , China Duration: 18 Aug 2010 → 22 Aug 2010

Conference

Conference	Fifth International Conference on Frontier of Computer Science and Technology (FCST), 2010
Country/Territory	China
City	Changchun, Jilin Province
Period	18/08/10 → 22/08/10

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10.1109/FCST.2010.57

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Pang, W & Coghill, GM 2010, Learning Qualitative Metabolic Models Using Evolutionary Methods. in 2010 Fifth International Conference on Frontier of Computer Science and Technology. IEEE Computer Society, Changchun, Jilin Province , pp. 436-441, Fifth International Conference on Frontier of Computer Science and Technology (FCST), 2010 , Changchun, Jilin Province , China, 18/08/10. https://doi.org/10.1109/FCST.2010.57

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abstract = "In this paper, an Evolutionary Qualitative Model Learning Framework (EQML) is proposed and tested by learning the qualitative metabolic models under the condition of incomplete knowledge. JMorven, a fuzzy qualitative reasoning engine, is slightly modified and integrated into the framework as a sub-module to represent and verify the candidate models. Three metabolic compartment models are tested by two evolutionary algorithms (Genetic Algorithm and Clonal Selection Algorithm) in EQML. Finally the efficiency of these two algorithms is evaluated. ",

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AU - Coghill, George MacLeod

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AB - In this paper, an Evolutionary Qualitative Model Learning Framework (EQML) is proposed and tested by learning the qualitative metabolic models under the condition of incomplete knowledge. JMorven, a fuzzy qualitative reasoning engine, is slightly modified and integrated into the framework as a sub-module to represent and verify the candidate models. Three metabolic compartment models are tested by two evolutionary algorithms (Genetic Algorithm and Clonal Selection Algorithm) in EQML. Finally the efficiency of these two algorithms is evaluated.

U2 - 10.1109/FCST.2010.57

DO - 10.1109/FCST.2010.57

M3 - Published conference contribution

SN - 978-1-4244-7779-1

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BT - 2010 Fifth International Conference on Frontier of Computer Science and Technology

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T2 - Fifth International Conference on Frontier of Computer Science and Technology (FCST), 2010

Y2 - 18 August 2010 through 22 August 2010

ER -

Learning Qualitative Metabolic Models Using Evolutionary Methods

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