基于粒子群算法的大DNA序列最大精确匹配算法

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mohamed Skander Daas, Billel Kenidra, Hamza Bouanaka, S. Chikhi
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引用次数: 0

摘要

随着完整哺乳动物基因组的出现,比较方法最近出现了热潮。在较大的DNA序列或数据库中搜索最大精确匹配是序列搜索中最常用的任务之一。已经设计了许多精确的算法来处理这个问题。这些算法所做的最佳改进导致了O(n)的时间和空间复杂性,并且它们对于大序列实际上仍然不那么有效。因此,启发式方法将是一个很好的实施替代方案。在这项工作中,我们提出了一种基于PSO元启发式的高效启发式算法来处理在大搜索序列中搜索小搜索序列的最大精确匹配问题。所设计算法的时间和空间复杂度为O(1)。实验结果表明,与其他两种序列搜索算法相比,所提出的搜索算法在大序列中寻找最大精确匹配的效率较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Efficient PSO-Based Algorithm for Finding Maximal Exact Match in Large DNA Sequences
With the appearance of complete mammalian genomes, comparative approaches have experienced a recent upsurge. Searching maximal exact match is among the most used tasks in sequence searching within a larger DNA sequence or database. Many exact algorithms have been designed to deal with this problem. The best improvements made by these algorithms have led to a time and space complexity of O(n) and they remain practically less effective for large sequences. Heuristic methods will therefore be a good alternative to implement. In this work, we present an efficient heuristic algorithm based on PSO metaheuristic to deal with the problem of searching the maximal exact match of small searched sequences in large ones. The time and space complexity of the designed algorithm is of O(1). The experimental results showed the efficiency of the proposed search algorithm for finding maximal exact match in large sequences when compared with two other sequence searching algorithms.
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
25
期刊介绍: The International Journal of Computational Intelligence and Applications, IJCIA, is a refereed journal dedicated to the theory and applications of computational intelligence (artificial neural networks, fuzzy systems, evolutionary computation and hybrid systems). The main goal of this journal is to provide the scientific community and industry with a vehicle whereby ideas using two or more conventional and computational intelligence based techniques could be discussed. The IJCIA welcomes original works in areas such as neural networks, fuzzy logic, evolutionary computation, pattern recognition, hybrid intelligent systems, symbolic machine learning, statistical models, image/audio/video compression and retrieval.
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