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An Efficient System for Finding Functional Motifs in Genomic DNA Sequences by Using Nature-Inspired Algorithms

机译:利用自然启发算法在基因组DNA序列中寻找功能基序的有效系统

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Motifs are short patterns in Deoxyribonucleic Acid (DNA) that indicate the presence of certain biological characteristics. Motifs finding is the process of successfully finding meaningful motifs in large DNA sequences. Nature-inspired algorithms have been recently gaining much popularity in solving complex and large real-world optimization problems similar to the motif finding problem. This work aims on investigating the application of nature-inspired algorithms in motif finding problem. The investigation methodology is divided into three main approaches; the first is to apply well-known nature-inspired algorithms in solving the problem, then the enhancement of an algorithm is investigated, and finally the hybridization between two algorithms is investigated. Experiments are performed on synthetic as well as real data sets. The results show that the combination provides the best results, however, individual and modified algorithms provide also good results compared to some state-of-the-art tools.
机译:图案是脱氧核糖核酸(DNA)的短图案,表明存在某些生物学特征。图案发现是在大型DNA序列中成功找到有意义的主题的过程。自然启发算法最近在解决与主题发现问题类似的复杂和大型现实世界优化问题方面取得了很大的普及。这项工作旨在调查自然启发算法在主题发现问题中的应用。调查方法分为三种主要方法;首先是在解决问题的情况下应用众所周知的自然启发算法,然后研究了算法的增强,最后研究了两种算法之间的杂交。实验是对合成和实际数据集进行的。结果表明,与某些最先进的工具相比,该组合提供了最佳结果,但是,与某些最先进的工具相比,个体和修改的算法也提供了良好的结果。

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