Biomedical literature mining: challenges and solutions in the 'omics' era.
Biomedical literature mining: challenges and solutions in the 'omics' era.
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DOI：
10.2165/00129785-200404060-00005
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发表时间：
2004-01-01
期刊：
影响因子：
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通讯作者：
Chaussabel, Damien
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文献类型：
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作者：
Chaussabel, Damien
现在很明显，高通量实验中的限速步骤既不是数据采集也不是分析，而是我们在全基因组范围内解释数据的能力。事实上，数据采样能力的爆炸性增长，加上出版率的不断提高，极大地削弱了我们在大量数据中发现意义的能力。为了支持数据解释，需要生物信息学工具来识别大量文献中包含的关键信息。然而，提取嵌入在自由文本中的知识是一项艰巨的任务，在生物医学领域，由于不一致的基因命名法，特定领域的语言和对全文文章的访问受限而变得更加复杂。本文介绍了目前可用的生物医学文献挖掘软件的选择。这些工具依赖于统计，最近，语义分析（自然语言处理）自动从文献中提取信息。此外，文献挖掘策略已经开发，以探索在摘要中出现的术语的模式。该方法自动识别摘要集合中的相关关键词，并使用模式发现算法生成用于探索基因之间的功能关联的可视化界面。术语出现热图也可以与基因表达谱相结合，以提供有价值的功能注释。此外，如肿瘤细胞系文献分析结果所示，这种方法可以应用于基因组数据分析之外的各种主题。总之，这些例子说明了如何文献分析可以用来支持生物医学研究中的知识发现。
It is now obvious that the rate-limiting step in high throughput experimentation is neither data acquisition nor analysis, but rather our ability to interpret data on a genome-wide scale. Indeed, the explosion of data sampling capacity combined with increasing publication rates greatly impairs our ability to find meaning in vast collections of data. In order to support data interpretation, bioinformatic tools are needed to identify critical information contained in large bodies of literature. However, extracting knowledge embedded in free text is an arduous task, compounded in the biomedical field by an inconsistent gene nomenclature, domain-specific language and restricted access to full text articles. This paper presents a selection of currently available biomedical literature mining software. These tools rely on statistic and, more recently, semantic analyses (Natural Language Processing) to automatically extract information from the literature. In addition, a literature mining strategy has been developed to explore patterns of term occurrences in abstracts. This method automatically identifies relevant keywords in collections of abstracts, and uses a pattern discovery algorithm to generate a visual interface for exploring functional associations among genes. Term occurrence heatmaps can also be combined with gene expression profiles to provide valuable functional annotations. Furthermore, as demonstrated with tumor cell line literature profiling results, this approach can be applied to a variety of themes beyond genomic data analysis. Altogether, these examples illustrate how literature analysis can be employed to support knowledge discovery in biomedical research.