Learning Qualitative Metabolic Models Using Evolutionary Methods

Author

Pang, Wei ; Coghill, George M.

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China

fYear

2010

fDate

18-22 Aug. 2010

Firstpage

436

Lastpage

441

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.

Keywords

biology computing; common-sense reasoning; evolutionary computation; fuzzy reasoning; learning (artificial intelligence); clonal selection algorithm; evolutionary algorithm; evolutionary method; evolutionary qualitative model learning framework; fuzzy qualitative reasoning engine; genetic algorithm; incomplete knowledge; learning qualitative metabolic model; Biological system modeling; Biological systems; Computational modeling; Data models; Evolutionary computation; Training data; Artificial Immune System; Genetic Algorithm; Qualitative Model Learning; Qualitative Reasoning;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Changchun, Jilin Province

Print_ISBN

978-1-4244-7779-1

Type

conf

DOI

10.1109/FCST.2010.57

Filename

5575786