Beyond information extraction: Identifying Gene Ontology concepts in text

描述(由申请人提供)： 近年来，人们对开发在科学文章的非结构化文本中自动识别基因本体论(GO)概念的方法越来越感兴趣。这种兴趣在一定程度上是因为需要使模型生物体数据库管理任务自动化。然而，此外，自动识别文本中围棋概念的方法将使数据挖掘工具能够编译和解释从文本中提取的信息，这些工具将使整个科学事业中的大量人受益。这个项目建立在最近完成的工作的基础上，在这些工作中，我们使用了酿酒酵母的文献和酵母基因组数据库(SGD)中的注释来开发方法，以确定一篇文章中提出了什么分子功能声明，以及文章中有哪些实验证据支持这些声明。该项目产生的数据包含丰富的信息，可以大大改进识别文本中围棋概念的方法。该项目的具体目标是：(1)开发GO分子功能概念的表示法，该表示法不仅捕获关于GO术语的语言的信息，而且还捕获该术语所指的生物医学实体的信息；以及(2)使用(1)中制定的GO表示法分析酿酒酵母数据挖掘项目的结果，以确定哪些可能产生改进的GO术语识别。将对276个真阳性结果、29,276个假阳性结果和336个假阴性结果进行分析，以查看新的围棋概念表示是否可以在不丢失任何真阳性的情况下减少假阳性或假阴性的数量。该建议的数据挖掘工具可以扩展到围棋以外的本体，从而利用本体开发上的努力。

DESCRIPTION (provided by applicant): There has been growing interest in recent years in developing methods that automatically identify Gene Ontology (GO) concepts in the unstructured text of scientific articles. This interest is motivated in part by the need to automate the task of model-organism database curation. In addition, however, methods that automatically identify GO concepts in text will enable data mining tools that compile and interpret information extracted from text, tools that will benefit a large number of people across the scientific enterprise. This project builds on recently completed work in which we used the literature of S. cerevisiae and annotations in the Saccharomyces Genome Database (SGD) to develop methods that determine what molecular function claims are being made in an article and what experimental evidence there is in the article for those claims. The data generated in this project contains a wealth of information that could lead to greatly improved methods for identifying GO concepts in text. The specific aims of this project are: (1) to develop a representation for GO molecular function concepts that captures information not only about the language of a GO term but also the biomedical entity the term refers to; and (2) to analyze the results of the S. cerevisiae data mining project using the GO representations formulated in (1) to determine which are likely to produce improved GO term recognition. The analysis will be performed on 276 true positive results, 29,276 false positive results, and 336 false negative results to see if a new GO concept representation can reduce the number of false positives or false negatives without losing any true positives. The data mining tools of this proposal can be extended to ontologies other than GO, thereby leveraging the effort expended on ontology development.