An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal

Authors:
Wei Pang;George M. Coghill
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, P.R. China 130012 and Department of Computing Science, University of Aberdeen, Aberdeen, UK AB24 3UE;Department of Computing Science, University of Aberdeen, Aberdeen, UK AB24 3UE
Venue:
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Year:
2009

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The Knowledge Engineering Review

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Abstract

In this paper, a qualitative model learning (QML) system is proposed to qualitatively reconstruct the detoxification pathway of Methylglyoxal. First a converting algorithm is implemented to convert possible pathways to qualitative models. Then a general learning strategy is presented. To improve the scalability of the proposed QML system and make it adapt to future more complicated pathways, an immune-inspired approach, a modified clonal selection algorithm, is proposed. The performance of this immune-inspired approach is compared with those of exhaustive search and two backtracking algorithms. The experimental results indicate that this approach can significantly improve the search efficiency when dealing with some complicated pathways with large-scale search spaces.