基于简单接近算法的蛋白质数据重复检测

Z. Zainol, Normaslina Taib
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引用次数: 0

摘要

由于生物数据库商业性和公共性的探索性,存在基因组数据不准确、不完整、重复和过时等缺点。必须做一些工作来保持基因组数据的质量。本文的目标是提供一个重复检测框架,该框架使用简单接近算法(SCA)来关注真实世界小鼠数据蛋白质数据库中的重复记录。SCA是对他们使用先验算法生成规则的工作的增强。在我们的方法中,我们使用矩阵结构来表示数据库中的数据。我们在1.4 GHz Pentium 4 PC机上使用Java编程语言实现SCA。因此,我们表明SCA可以比先验生成更少的重复规则(非冗余),而不会丢失信息,还可以改善执行时间
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Duplicate Detection for Protein Data Using Simple Close Algorithm
Due to the exploratory nature of biological database commercial and publicly, they are invited some drawback such as inaccurate, incomplete, duplicate and outdated of the genome data. Some work must be done to maintain quality data in genome data. Our objective in this paper is to provide a duplicate detection framework which focus on duplicate records in a real world Mice data protein database using simple close algorithm (SCA). SCA is an enhancement of work where they used a priori algorithm to generate the rule. In our approach we used matrix structure to represent data in a database. We implement SCA using Java programming language on 1.4 GHz Pentium 4 PC machine. As a result we show SCA can produces less duplicate rule (non-redundant) than a priori without loss of information and also improves the execution time
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