CoPub Mapper: mining MEDLINE based on search term co-publication
CoPub Mapper: mining MEDLINE based on search term co-publication
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DOI：
10.1186/1471-2105-6-51
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发表时间：
2005-03-11
影响因子：
3
通讯作者：
Jenster, G
中科院分区：
文献类型：
--
作者：
Alako, BTF;Veldhoven, A;Jenster, G
背景：高通量微阵列分析产生了许多差异表达的基因，这些基因可能与感兴趣的生物过程有关。结果：检索到15621个已知基因和3731个关键词的MEDLINE检索字符串。从MEDLINE检索PubMed ID，并确定所有基因-基因和基因-关键字对同时出现的相对概率。根据文献联合发表来评估基因聚集性，150个基因由8组已知连接(相同的途径、相同的蛋白质复合体或相同的细胞定位等)组成。都是通过程序运行的。接收算子特征(ROC)分析表明，大多数基因集随机聚类比预期的要好得多。为了从实际的微阵列数据中测试基因分组，使用CoPub Mapper分析了微阵列实验中的221个差异表达基因，得到了几个与生物学过程和疾病关键字相关的基因簇。此外，对所有基因和关键字进行了层次聚类，揭示了基于共现的已发表基因的完整分组。结论：CoPub Mapper程序允许快速、通用地查询共同发布的基因和关键字，可以成功地用于聚类预定义的基因组和微阵列数据。
Background: High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned.Results: MEDLINE search strings for 15,621 known genes and 3,731 keywords were generated and validated. PubMed IDs were retrieved from MEDLINE and relative probability of co-occurrences of all gene-gene and gene-keyword pairs determined. To assess gene clustering according to literature co-publication, 150 genes consisting of 8 sets with known connections ( same pathway, same protein complex, or same cellular localization, etc.) were run through the program. Receiver operator characteristics (ROC) analyses showed that most gene sets were clustered much better than expected by random chance. To test grouping of genes from real microarray data, 221 differentially expressed genes from a microarray experiment were analyzed with CoPub Mapper, which resulted in several relevant clusters of genes with biological process and disease keywords. In addition, all genes versus keywords were hierarchical clustered to reveal a complete grouping of published genes based on co-occurrence.Conclusion: The CoPub Mapper program allows for quick and versatile querying of co-published genes and keywords and can be successfully used to cluster predefined groups of genes and microarray data.