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Measuring Sequence Similarity Trough Many-to-Many Frequent Correlations

机译:测量序列相似性槽多对多频繁相关性

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Comparing pairs of sequences is a problem emerging in several application areas (ranging from molecular biology, to signal processing, text retrieval, and intrusion detection, just to cite a few) and important results have been achieved through the years. In fact, most of the algorithms in the literature rely on the assumption that matching symbols (or at least a substitution schema among them) are known in advance. This paper opens the way to a more involved mechanism for sequence comparison, where determining the best substitution schema is also part of the matching problem. The basic idea is that any symbol of one sequence can be correlated with many symbols of the other sequence, provided each correlation frequently occurs over the various positions. The approach fits a variety of problems difficult to be handled with classical techniques, particularly where strings to be matched are defined over different alphabets.
机译:比较序列对是在几个应用领域出现的问题(从分子生物学的范围内,信号处理,文本检索和入侵检测,只是为了引用几年而且重要的结果已经实现了多年。实际上,文献中的大多数算法依赖于预先已知匹配符号(或至少替换模式)的假设。本文打开了更涉及的序列比较机制的方式,其中确定最佳替换模式也是匹配问题的一部分。基本思想是,一个序列的任何符号可以与其他序列的许多符号相关,只要每个相关性经常发生在各个位置上。该方法适合具有经典技术难以处理的各种问题,特别是在要匹配的字符串的情况下定义在不同的字母表上。

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