探勘生物序列循序樣式的高效率方法

廖強棋

doi:10.6342/NTU.2013.02647

山西大学，您好！

感谢采购，人社期刊库｜人社论文库，期间：2025/01/01~2025/12/31

感谢试用，科学期刊库｜科学论文库，期间：2024/10/20~2025/01/20

浏览高级检索

关闭
检索检索高级检索出版物检索
浏览期刊主题浏览核心期刊浏览期刊拼音浏览

关闭

查询符号(半角)	查询符号意义说明
空格	表示为「AND」两个查询词之交集
双引号 ( " " )	片语以双引号标示开始及结束，而且只寻找出现顺序相同的字词，例： " image process "
?	表示一个字母切截，输入两个?表两个字母，依此类推，例：输入「Appl?」，查得结果应为appl e , appl y … ( 常用于英文字查询 )
*	表示不限字母切截，由0~n. 例：输入「appl*」，查得结果应为appl e , appl es , appl y , appl ied , appl ication … ( 常用于英文字查询 )
AND、OR、NOT	布尔逻辑组合关键字，用来扩大或缩小查询范围的技巧。 (1) AND ：缩小查询范围 (2) OR ：扩大查询范围(3) NOT：排除不相关的范围

DOI 是数位物件识别码（ D igital O bject I dentifier ）的简称，
为物件在网路上的唯一识别码，可用于永久连结并引用目标物件。

使用DOI作为永久连结

每个DOI号前面加上「 http://dx.doi.org/ 」便成为永久网址。
如以DOI号为 10.5297/ser.1201.002 的文献为例，此文献的永久连结便是： http://dx.doi.org/ 10.5297/ser.1201.002 。
日后不论出版单位如何更动此文献位置，永久连结所指向的位置皆会即时更新，不再错失重要的研究。

引用含有DOI的文献

有DOI的文献在引用时皆应同时引用DOI。若使用APA、Chicago以外未规范DOI的引用格式，可引用DOI永久连结。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。

书目管理工具

相关连结

问题回报

全文下载

探勘生物序列循序样式的高效率方法

Efficient Methods for Mining Sequential Patterns of Biological Sequences

廖强棋 , 博士　　导师：陈铭宪　　

英文 DOI： 10.6342/NTU.2013.02647

循序样式；结构模体；样式探勘；资料探勘；生物资讯； Sequential patterns ； Structured motifs ； Pattern mining ； Data mining ； Bioinformatics

0 个人觉得这篇文章赞

摘要 │ 参考文献 (57) │ 国际计量

并列摘要〈TOP〉

Scientific progress in recent years has led to the generation of huge amounts of biological data, most of which remains unanalyzed. Mining the data may provide insights into various realms of biology, such as finding co-occurring biosequences, which are essential for biological data mining and analysis. Data mining techniques like sequential pattern mining may reveal implicitly meaningful patterns among the DNA or protein sequences. 
Pattern mining for biological sequences is an important problem in bioinformatics and computational biology. Sequential pattern mining can reveal all-length motifs in biological sequences. Performing sequential pattern mining on biological sequences helps reveal implicit motifs/patterns, which are usually of functional significance and have specific structures. If biologists hope to uncover the potential of sequential pattern mining in their field, it is necessary to move away from traditional sequential pattern mining algorithms since these algorithms have difficulty in handling small alphabets and long sequence lengths in biological data, such as gene and protein sequences. To tackle the problem, this dissertation proposes an approach called Depth-First SPelling (DFSP) algorithm for mining sequential patterns in biological sequences. DFSP is a general model for mining sequential patterns of biological sequences. The algorithm’s processing speed is faster than that of PrefixSpan, its leading competitor, and DFSP is superior to other sequential pattern mining algorithms for biological sequences.
Furthermore, gap constraints are important in computational biology since they cope with irrelative regions, which are not conserved in evolution. An approach is devised to efficiently mine sequential patterns (motifs) of biological sequences with gap constraints in this dissertation. The approach is called the Depth-First Spelling algorithm for mining sequential patterns with Gap constraints in biological sequences (referred to as DFSG). DFSG is a general gap constraint model in sequential pattern mining of biological sequences. GenPrefixSpan is a method based on PrefixSpan with gap constraints, and therefore this dissertation compares DFSG with GenPrefixSpan. In biological sequences, DFSG’s runtime is substantially shorter than that of GenPrefixSpan. 
The intra- and inter-block gap constraints are shown to effectively cope with the substitutions, insertions, loops, and deletions involved in evolution, but induce even higher computation cost. Hence this dissertation presents an approach called Depth-first spelling algorithm for mining structured motifs with Intra- and inter-Block gap constraints in biological sequences (referred to as DIB) to tackle this challenge. When mining biological sequences, DIB’s runtime is shown to be much shorter than a previously developed projection-based sequential pattern mining algorithm, MAGIIC.

参考文献 ( 57 ) 〈TOP〉

[1] A. Achar, S. Laxman, and P. S. Sastry, A unified view of the apriori-based algorithms for frequent episode discovery. Knowledge and Information Systems, pages 223-250, 2011.
连结：
[4] C. Antunes and A. Oliveira, Generalization of Pattern-growth Methods for Sequential Pattern Mining with Gap Constraints, Proceedings of the International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM’03), Lecture Notes in Computer Science, Leipzig, Germany, pages 239–251, 2003.
连结：
[5] S. Aseervatham, A. Osmani, and E. Viennet. bitSPADE: A latticebased sequential pattern mining algorithm using bitmap representation. Proceedings of 6th International Conference on Data Mining, pages 792-797, 2006.
连结：
[8] I. Batal, H. Valizadegan, G. F. Cooper and M. Hauskrecht, A Pattern Mining Approach for Classifying Multivariate Temporal Data, Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2011.
连结：
[10] A. Califano, SPLASH: structural pattern localization analysis by sequential histograms. Bioinformatics, 16(4): 341-357, 2000.
连结：

国际计量

〈TOP〉

E-mail ：

文章公开取用时，将寄通知信至您填写的信箱地址

E-mail ：

使用DOI作为永久连结

引用含有DOI的文献

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。 如想对DOI的使用与概念有进一步了解，请参考 （ ） 。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。