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Hybrid Artificial Bee Colony and Particle Swarm Optimization Approach to Protein Secondary Structure Prediction

机译:混合人工蜂群和粒子群算法在蛋白质二级结构预测中的应用

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Proteins are crucial in the biological processes, and their structure determines whether they can function well or not. Since the theory presented by Anfinsen that proteins' space structure is entirely determined by the primary structure came out, it is possible for us to predict the structure of proteins through their primary structure without any experiment. In order to reach this target, the prediction problem can be formulated as an optimization problem that is set to find the lowest free energy conformation. In this paper, a hybrid Artificial Bee Colony (ABC) with Particle Swarm Optimization (PSO) Algorithm is used to solve this problem. Considering that the two algorithms have complementary characteristics, we combine them together and find out a better optimization results through this new approach. Experimental results have demonstrated the feasibility and effectiveness of our proposed approaches.
机译:蛋白质在生物过程中至关重要,蛋白质的结构决定了蛋白质能否正常发挥作用。自从安芬森提出的蛋白质的空间结构完全由一级结构决定的理论问世以来,我们有可能通过其一级结构预测蛋白质的结构而无需进行任何实验。为了达到这个目标,可以将预测问题表述为优化问题,该优化问题设置为查找最低的自由能构象。本文采用带粒子群算法(PSO)的混合人工蜂群(ABC)解决了该问题。考虑到两种算法具有互补性,我们将它们组合在一起,并通过这种新方法找到了更好的优化结果。实验结果证明了我们提出的方法的可行性和有效性。

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