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 › 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
Publisher	IEEE
Number of pages	6
ISBN (Print)	978-1-4244-7779-1
DOIs	https://doi.org/10.1109/FCST.2010.57
Publication status	Published - 2010

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10.1109/FCST.2010.57

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title = "Learning Qualitative Metabolic Models Using Evolutionary Methods",

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.",

author = "Wei Pang and 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 - Conference contribution

SN - 978-1-4244-7779-1

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

PB - IEEE

ER -

Learning Qualitative Metabolic Models Using Evolutionary Methods

Abstract

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