生物序列搜索的RSMA匹配算法

Ahmad F. Klaib, H. Osborne
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引用次数: 3

摘要

大量的生物数据存储在线性文件中。生物蛋白质是氨基酸的序列。这些领域的数据量有逐年增加的趋势。字符串匹配算法在许多计算机科学问题和计算机软件的实现中起着关键作用。出于这个原因,应该使用高效的字符串匹配算法,这些算法使用最小的计算机存储并最小化搜索响应时间。在这项研究中,我们提出了一种新的算法,称为随机字符串匹配算法(RSMA)。RSMA将Berry Ravindran算法的增强预处理阶段与我们提出的新搜索阶段过程相结合。这种不同的搜索顺序使得我们提出的算法减少了比较字符的数量,提高了搜索响应时间。实验结果表明,与其他已知算法相比,RSMA算法提供了更少的比较次数,并缩短了运行时间。
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RSMA Matching Algorithm for searching biological sequences
Huge amounts of biological data are stored in linear files. Biological proteins are sequences of amino acids. The quantities of data in these fields tend to increase year on year. String matching algorithms play a key role in many computer science problems, and in the implementation of computer software. For this reason efficient string-matching algorithms should be used which use minimal computer storage and which minimize the searching response time. In this study, we propose a new algorithm called the Random String Matching Algorithm (RSMA). RSMA combines our enhanced preprocessing phase from the Berry Ravindran algorithm with our proposed new searching phase procedure. This variety of searching order allows our proposed algorithm to reduce the number of comparison characters and enhances the searching response time. Experimental results show that the RSMA algorithm offers a smaller number of comparisons and offers improved elapsed searching time when compared to other well-known algorithms.
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