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使用基因本体进行微阵列数据挖掘。

Microarray data mining using gene ontology.

作者信息

Li S, Becich M J, Gilbertson J

机构信息

Center for Pathology Informatics, Department of Pathology, Benedum Oncology Informatics Center, University of Pittsburgh, Cancer Institute, University Pittsburgh, Medical School, 15232, USA.

出版信息

Stud Health Technol Inform. 2004;107(Pt 2):778-82.


DOI:
PMID:15360918
Abstract

DNA microarray technology allows scientists to study the expression of thousands of genes--potentially entire genomes--simultaneously. However the large number of genes, variety of statistical methods employed and the complexity of biologic systems complicate analysis of microarray results. We have developed a web based environment that simplifies the presentation of microarray results by combining microarray results processed for statistical significance with probe set annotation by Genbank, NCBI RefSeqs, GeneCards and the Gene Ontology. This allows rapid examination and classification of microarray experiments--annotated by NCIBI tools --by Statistical Significance and Gene Oncology Classes. By providing a simple, easily understood interface to large microarray data sets, this tool has been particularly useful for small research groups focused on a small number of related genes and for researchers who want to ask simple questions without the overhead of complex data management and analysis.

摘要

DNA微阵列技术使科学家能够同时研究数千个基因(甚至可能是整个基因组)的表达情况。然而,基因数量众多、所采用的统计方法多样以及生物系统的复杂性,使得微阵列结果的分析变得复杂。我们开发了一个基于网络的环境,通过将经统计学显著性处理的微阵列结果与来自Genbank、NCBI RefSeqs、GeneCards和基因本体论的探针集注释相结合,简化了微阵列结果的呈现。这使得能够通过统计学显著性和基因肿瘤学类别对由NCIBI工具注释的微阵列实验进行快速检查和分类。通过为大型微阵列数据集提供一个简单易懂的界面,该工具对于专注于少数相关基因的小型研究团队以及想要提出简单问题而无需复杂数据管理和分析负担的研究人员特别有用。

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