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题名: Comparison between two PCR-based bacterial identification methods through artificial neural network data analysis
作者: Wen, Jie2;  Zhang, Xiaohui2;  Gao, Peng1;  Jiang, Qiuhong2
关键词: 16S rRNA gene ;  16S-23S rRNA spacer region gene ;  capillary electrophoresis ;  artificial neural network (ANN) ;  single-strand conformation polymorphism (SSCP) ;  restriction fragment length polymorphism (RFLP)
刊名: JOURNAL OF CLINICAL LABORATORY ANALYSIS
发表日期: 2008
DOI: 10.1002/jcla.20224
卷: 22, 期:1, 页:14-20
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology ;  Life Sciences & Biomedicine
类目[WOS]: Medical Laboratory Technology
研究领域[WOS]: Medical Laboratory Technology
英文摘要: The 16S ribosomal ribonucleic acid (rRNA) and 16S-23S rRNA spacer region genes are commonly used as taxonomic and phylogenetic tools. In this study, two pairs of fluorescent-labeled primers for 16S rRNA genes and one pair of primers for 16S-23S rRNA spacer region genes were selected to amplify target sequences of 317 isolates from positive blood cultures. The polymerase chain reaction (PCR) products of both were then subjected to restriction fragment length polymorphism (RFLP) analysis by capillary electrophoresis after incomplete digestion by Hae III. For products of 16S rRNA genes, single-strand conformation polymorphism (SSCP) analysis was also performed directly. When the data were processed by artificial neural network (ANN), the accuracy of prediction based on 16S-23S rRNA spacer region gene RFLP data was much higher than that of prediction based on 16S rRNA gene SSCP analysis data(98.0% vs. 79.6%). This study proved that the utilization of ANN as a pattern recognition method was a valuable strategy to simplify bacterial identification when relatively complex data were encountered.
关键词[WOS]: 16S RIBOSOMAL-RNA ;  STRAND CONFORMATION POLYMORPHISM ;  RAPID IDENTIFICATION ;  SEQUENCE-ANALYSIS ;  ELECTROPHORESIS ;  GENE ;  POLYACRYLAMIDE ;  MICROARRAY ;  PATHOGENS ;  REGION
语种: 英语
WOS记录号: WOS:000253587800003
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/140790
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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作者单位: 1.Chinese Acad Sci, Dalian Inst Chem Phys, Natl Chromatog R&A Ctr, Dalian 116023, Peoples R China
2.Dalian Municipal Cent Hosp, Dalian, Peoples R China

Recommended Citation:
Wen, Jie,Zhang, Xiaohui,Gao, Peng,et al. Comparison between two PCR-based bacterial identification methods through artificial neural network data analysis[J]. JOURNAL OF CLINICAL LABORATORY ANALYSIS,2008,22(1):14-20.
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