Learning Qualitative Metabolic Models Using Evolutionary Methods

Wei Pang, George MacLeod Coghill

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

2 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 languageEnglish
Title of host publication2010 Fifth International Conference on Frontier of Computer Science and Technology
PublisherIEEE
Number of pages6
ISBN (Print)978-1-4244-7779-1
DOIs
Publication statusPublished - 2010

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    Pang, W., & Coghill, G. M. (2010). Learning Qualitative Metabolic Models Using Evolutionary Methods. In 2010 Fifth International Conference on Frontier of Computer Science and Technology IEEE. https://doi.org/10.1109/FCST.2010.57