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.
|Title of host publication||ICARIS 2009: Artificial Immune Systems|
|Editors||Paul S. Andrews|
|Number of pages||14|
|Publication status||Published - 2009|
|Name||Lecture Notes in Computer Science|