Abstract
In this paper, a special-purpose qualitative model learning (QML) system using an immune-inspired algorithm is proposed to qualitatively reconstruct biological pathways. We choose a real-world application, the detoxification pathway of Methylglyoxal (MG), as a case study. First a converter 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, a modified clonal selection algorithm (CLONALG) is employed as the search strategy. The perfor- mance of this immune-inspired approach is compared with those of exhaustive search and two backtracking algorithms. The experimental results indicate that this immune-inspired approach can significantly improve the search efficiency when dealing with some complicated pathways with large-scale search spaces.
Original language | English |
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Pages (from-to) | 189-207 |
Number of pages | 19 |
Journal | Natural Computing |
Volume | 10 |
Issue number | 1 |
Early online date | 6 Aug 2010 |
DOIs | |
Publication status | Published - Mar 2011 |
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Keywords
- clonal selection algorithm
- immune-inspired algorithm
- pathway reconstruction
- qualitative differential equation
- qualitative model learning
- qualitative reasoning
- qualitative simulation