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启动子识别问题是生物信息学中重要研究问题之一。由于生物序列的复杂性和特殊性,研究更符合生物学数据特征的启动子发现方法具有重要意义。十字花科黑腐病菌是一种能引起所有十字花科植物黑腐病的重要病原细菌。本文建立3目标的优化计算模型,运用基于带精英策略的非支配排序遗传算法设计启动子识别算法对十字花科黑腐病菌数据集中的保守片段(Motif)进行挖掘。实验结果表明,与已有的相关算法相比,本文给出的算法无须指定候选启动子序列长度,获得了多组不同支持度的候选启动子序列供决策者选择,算法高效、扩展性好,可以适用于求解多种不同长度序列数据的启动子识别问题。
Promoter recognition is one of the most important research issues in bioinformatics. Due to the complexity and particularity of biological sequences, it is of great importance to study promoter discovery methods that are more in line with the characteristics of biological data. Cruciferae is one of the most important pathogenic bacteria that causes black rot in all cruciferous plants. In this paper, a 3-objective optimization model was established, and a promoter recognition algorithm was designed based on the non-dominated ranking genetic algorithm with elitist strategy to excavate the conserved motif in the crucifer black rot fungus dataset. The experimental results show that compared with the existing algorithms, the proposed algorithm does not need to specify the length of the candidate promoter sequence, and obtains multiple candidate promoter sequences with different degrees of support for the decision makers to choose. The algorithm is efficient, scalable, It can be used to solve the promoter recognition problem of many different length sequence data.