Use of keyword hierarchies to interpret gene expression patterns
Use of keyword hierarchies to interpret gene expression patterns
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DOI：
10.1093/bioinformatics/17.4.319
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发表时间：
2001-04-01
期刊：
影响因子：
5.8
通讯作者：
Corbeil, J
中科院分区：
文献类型：
--
作者：
Masys, DR;Welsh, JB;Corbeil, J
动机：高密度微阵列技术允许对数千个基因进行定量和同时监测。解释的挑战是从大量数据中提取相关信息。越来越多的统计分析方法可用于识别具有共同表达特征的基因簇，但没有提供关于簇内基因的生物学相似性的信息。已发表的文献提供了一个潜在的信息来源，以协助解释聚类results.Results：我们描述了一种数据挖掘方法，使用索引术语（“关键字”）从已发表的文献链接到特定的基因，提出一个视图的概念相似性的基因内的一个集群或组的利益。该方法利用了用于索引MEDLINE数据库中引文的医学主题词的层次性质，以及应用于酶的注册号。
Motivation: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results.Results: We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes.