Mining functional information associated with expression arrays
Mining functional information associated with expression arrays
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DOI：
10.1007/s101420000036
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发表时间：
2001-03-01
影响因子：
2.9
通讯作者：
Valencia, Alfonso
中科院分区：
文献类型：
--
作者：
Blaschke, Christian;Oliveros, Juan C.;Valencia, Alfonso
随着在基因组尺度上监测基因表达模式，破译生物系统中分子之间相互作用的网络已经获得了动力。表达阵列实验提供了大量关于这些网络的实验数据，对其进行分析需要新的计算方法。特别是，与生物信息提取有关的问题对最终用户至关重要。我们在这里提出了一个策略，在一个系统中实施称为GEISHA（人类分析的基因表达信息系统），并能够检测生物学术语显着相关的不同基因表达集群通过挖掘收集的Medline摘要。GEISHA基于与不同基因簇相关并包含给定术语的摘要频率的比较。通过将术语嵌入相应的重要句子和摘要中，并与其他同等重要的术语建立关系，便于最终用户解释术语的生物学含义。GEISHA为可用的酵母表达数据提供的信息与人类专家提供的功能注释相比毫不逊色，证明了GEISHA作为表达阵列实验分析助手的潜在价值。
Deciphering the networks of interactions between molecules in biological systems has gained momentum with the monitoring of gene expression patterns at the genomic scale. Expression array experiments provide vast amounts of experimental data about these networks, the analysis of which requires new computational methods. In particular, issues related to the extraction of biological information are key for the end users. We propose here a strategy, implemented in a system called GEISHA (gene expression information system for human analysis) and able to detect biological terms significantly associated to different gene expression clusters by mining collections of Medline abstracts. GEISHA is based on a comparison of the frequency of abstracts linked to different gene clusters and containing a given term. Interpretation by the end user of the biological meaning of the terms is facilitated by embedding them in the corresponding significant sentences and abstracts and by establishing relations with other, equally significant terms. The information provided by GEISHA for the available yeast expression data compares favorably with the functional annotations provided by human experts, demonstrating the potential value of GEISHA as an assistant for the analysis of expression array experiments.