基于主动宾中间体挖掘的蛋白质相互作用路径重构
Reconstruction of Protein-Protein Interaction Pathways by Mining Subject-Verb-Objects Intermediates
摘要：
新文章发表率的指数增长限制了研究人员对相关文献的访问。这促使人们使用文本挖掘工具来提取关键的生物信息。以前的研究报告了现有的通用文本处理器的广泛修改，以处理生物文本。然而，这一修改要求尚未得到审查。在这项研究中，我们构建了Muscorian，使用MontyLingua，一个通用的文本处理器。它使用了一个两层的泛化-专业化范式先前提出的文本一般处理到一个合适的中间格式之前，特定于域的数据提取技术应用在专业化层。使用语料库和专家进行的评估表明，在提取蛋白质-蛋白质相互作用方面，精确度为86-90%，召回率约为30%，这与使用专门的生物文本处理工具或修改的现有工具的先前研究相当。我们的研究还通过使用同一个泛化层来完成两个特定的信息抽取任务，证明了两层泛化-特定化范式的灵活性。
英文摘要：
The exponential increase in publication rate of new articles is limiting access of researchers to relevant literature. This has prompted the use of text mining tools to extract key biological information. Previous studies have reported extensive modification of existing generic text processors to process biological text. However, this requirement for modification had not been examined. In this study, we have constructed Muscorian, using MontyLingua, a generic text processor. It uses a two-layered generalization-specialization paradigm previously proposed where text was generically processed to a suitable intermediate format before domain-specific data extraction techniques are applied at the specialization layer. Evaluation using a corpus and experts indicated 86-90% precision and approximately 30% recall in extracting protein-protein interactions, which was comparable to previous studies using either specialized biological text processing tools or modified existing tools. Our study had also demonstrated the flexibility of the two-layered generalization-specialization paradigm by using the same generalization layer for two specialized information extraction tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
