Analysis of genomic and proteomic data using advanced literature mining
Analysis of genomic and proteomic data using advanced literature mining
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DOI：
10.1021/pr0340227
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发表时间：
2003-07-01
影响因子：
4.4
通讯作者：
LaBaer, J
中科院分区：
文献类型：
--
作者：
Hu, YH;Hines, LM;LaBaer, J
蛋白质组筛选和 DNA 微阵列等高通量技术会产生大量数据，需要综合分析方法来解读生物学相关结果。一种方法是手动搜索生物医学文献；然而，这将是一项艰巨的任务。我们开发了一种自动化文献挖掘工具，称为 MedGene，它全面总结和估计了 Medline 中所有人类基因与疾病关系的相对优势。使用 MedGene，我们分析了一种新型微阵列表达数据集，在现有知识的背景下比较乳腺癌和正常乳腺组织。当考虑高达 5 倍的变化时，我们发现文献关联的强度与表达水平差异的大小之间没有相关性；然而，在表现出 10 倍或更多表达差异的基因之间观察到显着相关性（r = 0.41；p = 0.05）。有趣的是，这只适用于雌激素受体（ER）阳性肿瘤，而不适用于 ER 阴性肿瘤。 MedGene 在 ER 阴性肿瘤中发现了一组相对未被充分研究但高表达的基因，值得进一步检查。
High-throughput technologies, such as proteomic screening and DNA micro-arrays, produce vast amounts of data requiring comprehensive analytical methods to decipher the biologically relevant results. One approach would be to manually search the biomedical literature; however, this would be an arduous task. We developed an automated literature-mining tool, termed MedGene, which comprehensively summarizes and estimates the relative strengths of all human gene-disease relationships in Medline. Using MedGene, we analyzed a novel micro-array expression dataset comparing breast cancer and normal breast tissue in the context of existing knowledge. We found no correlation between the strength of the literature association and the magnitude of the difference in expression level when considering changes as high as 5-fold; however, a significant correlation was observed (r = 0.41; p = 0.05) among genes showing an expression difference of 10-fold or more. Interestingly, this only held true for estrogen receptor (ER) positive tumors, not ER negative. MedGene identified a set of relatively understudied, yet highly expressed genes in ER negative tumors worthy of further examination.