Gene clustering by Latent Semantic Indexing of MEDLINE abstracts
Gene clustering by Latent Semantic Indexing of MEDLINE abstracts
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DOI：
10.1093/bioinformatics/bth464
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发表时间：
2005-01-01
期刊：
影响因子：
5.8
通讯作者：
Berry, MW
中科院分区：
文献类型：
--
作者：
Homayouni, R;Heinrich, K;Berry, MW
动机：解释高通量基因组数据的一个主要挑战是理解基因之间的功能关联。在此之前，已有几种方法描述了使用术语匹配方法从各种生物数据库中提取基因关系。然而，需要更灵活的自动化方法来识别生物医学文献中基因之间的功能关系（包括显性和隐性）。在这项研究中，我们探索了潜在语义索引（LSI）的应用，这是一种用于信息检索的向量空间模型，可以自动从MEDLINE引文的标题和摘要中识别概念基因关系。结果：大规模集成电路对基因间关系和关键字与基因间关系的识别具有较高的平均精度。此外，LSI还根据基因摘要文档中的单词使用模式确定了隐性基因关系。最后，我们在这里证明了从基因抽象文档的矢量角度得出的两两距离可以有效地用于通过层次聚类对基因进行功能分组。我们的结果提供了原理证明，LSI是一种强大的自动化方法，可以阐明生物医学文献中已知（显式）和未知（隐式）的基因关系。这些特点使得LSI对基因组实验中发现的新关联的分析特别有用。
Motivation: A major challenge in the interpretation of high-throughput genomic data is understanding the functional associations between genes. Previously, several approaches have been described to extract gene relationships from various biological databases using term-matching methods. However, more flexible automated methods are needed to identify functional relationships (both explicit and implicit) between genes from the biomedical literature. In this study, we explored the utility of Latent Semantic Indexing (LSI), a vector space model for information retrieval, to automatically identify conceptual gene relationships from titles and abstracts in MEDLINE citations.Results: We found that LSI identified gene-to-gene and keyword-to-gene relationships with high average precision. In addition, LSI identified implicit gene relationships based on word usage patterns in the gene abstract documents. Finally, we demonstrate here that pairwise distances derived from the vector angles of gene abstract documents can be effectively used to functionally group genes by hierarchical clustering. Our results provide proof-of-principle that LSI is a robust automated method to elucidate both known (explicit) and unknown (implicit) gene relationships from the biomedical literature. These features make LSI particularly useful for the analysis of novel associations discovered in genomic experiments.