Finding LPRs in DNA Sequence Based on a New Index &amp;#8212; SUA

Wang, Di; Wang, Guoren; Wu, Qingquan; Chen, Baichen

doi:10.1109/bibe.2005.31

Cited by 11 publications

(11 citation statements)

References 7 publications

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“…Signature-based identification is not new to the DNA area. However, their focus is to find some sub-sequence [5][6][7] or PTR (perfect tandem repeat) [8][9][10][11] until the support does not go below a certain threshold. Meanwhile, we focus on how to find frequent substring that satisfies the condition, not sub-sequence.…”

Section: Related Workmentioning

confidence: 99%

STAFF: Automated Signature Generationfor Fine-Grained FunctionTraffic Identification

Tang

Chen

2018

JWE

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Identifying a user operating application function can reflect the user behavior, or even can help to improve the user experience. It is the focus of the real application in big data analytics technology. Unlike Coarse-grained Traffic Identification (CTI) which only identify application/protocol that a packet is related to, Fine-grained Function Traffic Identification (FFTI) maps the traffic packet to a meaningful user operation or an application function. In this paper, our focus is to identify the fine-grained function signature. We propose an automatic and stable signature generation method, so-called STAFF, to identify different application functions. STAFF treats data packets as long strings. The aim of our method is to find all the string fragments whose length is longer than a prescribed length and whose occurrence is higher than a prescribed frequency. The final signature will be presented as pairs of string fragments and their corresponding occurrence frequency. The experimental results show that STAFF can automatically generate finegrained function signatures in different applications with average 93.65% identification accuracy and the method is noise insensitive.

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Section: Related Workmentioning

confidence: 99%

STAFF: Automated Signature Generationfor Fine-Grained FunctionTraffic Identification

Tang

Chen

2018

JWE

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“…LPR(largest pattern repetition),并设计了索引结构后继数组(succeeding unit array,简称SUA)完成LPR的查找 [73] . 然而,由于DNA序列存在突变等情况,导致这些重复成为非精确相等的.已有的PTR发现,算法无法发现这样的近似串联重复序列ATR.于是,出现了大量ATR发现算法 [19,70,74−81] ,如Beason设计的TRFinder算法是最有影响力的串联重复序列发现算法 [70] ;Kurtz等人提出的REPuter [78] 基于后缀树算法克服了输入序列大小限制,但它基于子序列两两比对,难以找到DNA序列中出现次数较高的重复序列;2005 年,Li等人提出了一种新的投影拼接算法有效识别重复序列 [82] ;2005 年,Ydo Wexel等人给出了关于评分函数的 3 种类型ATRs的定义,给定一个评分函数Φ,…”

Section: 当多的研究成果例如 2005 年Wang等人提出了针对精确查找的新的重复片段的定义--最大模式重复unclassified

DNA Sequence Data Mining Technique

Zhu

Xiong²

2007

Journal of Software

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DNA sequence is one of the basic and important data among biological data. Researching DNA sequence data and then comprehending life essential is a necessary task in post-genomic era. At present, data mining technique is one of the most efficient data analysis means, which finds out information hidden in data. It has also become main data analysis technique adopted in Bioinformatics. It has been applied in DNA sequence analysis, which has got wide attention and rapid development. And considerable research achievements have emerged. Provides an overview of research progress in DNA sequence data mining field. In more detail, it proposes three research phases including statistics-based data mining methods application, general data mining methods application, and specialized DNA sequence-oriented data mining methods design, and then elaborates that sequence similarity is foundation of DNA sequence data mining technique. It also analyzes and comments some key techniques in this field by combining with biological background, such as DNA sequential pattern, association, clustering, classification and outlier mining. Finally, future work and open issues are given, including the research of a novel storage model and index methods, the design of data mining algorithm based on biological domain knowledge.

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“…In those DNA sequences disperse iterations of nucleotide motifs called Tandem Repeats (TRs). TRs' genetic and evolutionary mechanisms remain controversial; however it is believed that they are functionally important for gene transcription, translation, chromatin organization, recombination, DNA replication, cell cycle, etc [4][5] [6] [6] [15]. Currently TRs, as important genetic makers, has been prevalently applied in realms such as paternity testing, forensic investigations and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Currently TRs, as important genetic makers, has been prevalently applied in realms such as paternity testing, forensic investigations and so on. Therefore, it is essential to find and study those TRs by using Data Mining technologies [2] [15].…”

Section: Introductionmentioning

confidence: 99%