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多序列比对问题是生物信息学的热点研究问题.针对大规模多序列比对精度低问题,提出基于概率统计自适应粒子群的生物多序列比对算法.根据优质解的分布概率建立模型用于引导粒子产生新解,使种群中的粒子具有更全面的学习能力,从而提高比对结果的精度;引入适应度方差、期望最优解和变异操作跳出早熟状态,避免算法陷入局部最优值.对BALIBASE中142个例子进行仿真,实验结果验证了算法的可行性和有效性,与已有的算法相比,该算法对大规模亲缘较近长序列比对问题具有更强的求解能力.
Multi-sequence alignment is a hot research topic in bioinformatics.In order to solve the problem of large-scale multi-sequence alignment, a multi-sequence alignment algorithm based on PSO is proposed.According to the distribution probability of good solution, Which leads to new solutions for guiding particles and makes the particles in the population have a more comprehensive learning ability so as to improve the accuracy of the comparison results. The fitness variance is introduced, and the optimal solution and mutation operation are expected to jump out of the precocious state and prevent the algorithm from falling into the local optimum In the simulation of 142 cases in BALIBASE, the experimental results verify the feasibility and effectiveness of the proposed algorithm, which has a better ability to solve large-scale kinship alignment problems than the existing ones.