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

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

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
PublisherSpringer
Pages151-164
Number of pages14
Volume5666
ISBN (Print)978-3-642-03245-5
DOIs
Publication statusPublished - 28 Jul 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume5666
ISSN (Print)0302-9743

Fingerprint Dive into the research topics of 'An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal'. Together they form a unique fingerprint.

  • Cite this

    Pang, W., & Coghill, G. M. (2009). An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal. In Lecture Notes in Computer Science (Vol. 5666, pp. 151-164). (Lecture Notes in Computer Science; Vol. 5666). Springer . https://doi.org/10.1007/978-3-642-03246-2_17