Beyond information extraction: Identifying Gene Ontology concepts in text

超越信息提取：识别文本中的基因本体概念

• 批准号: 7362877
• 负责人: Colleen Elizabeth Crangle
• 金额: $ 5万
• 依托单位: CONVERSPEECH, LLC
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7362877
• 关键词: Biological Assay; Biological Process; Controlled Vocabulary; Data; Data Set; Databases; Development; Ensure; Frequencies; Genes; Individual; Information Retrieval; Language; Lead; Literature; Methods; Molecular; Ontology; Problem Solving; Proteins; Reporting; Saccharomyces; Saccharomyces cerevisiae; Text; Work; base; biomedical scientist; data mining; gene correction; gene function; genome database; improved; interest; model organisms databases; natural language; phrases; success; text searching; tool

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.

描述（由申请人提供）： 近年来，人们对开发自动识别科学文章非结构化文本中的基因本体（GO）概念的方法越来越感兴趣。这种兴趣部分是由于需要自动化模型生物数据库管理任务。然而，此外，自动识别文本中的 GO 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，这些工具将使科学事业中的大量人员受益。该项目建立在最近完成的工作的基础上，其中我们使用酿酒酵母文献和酵母基因组数据库 (SGD) 中的注释来开发方法来确定文章中提出的分子功能主张以及文章中针对这些主张有哪些实验证据。该项目生成的数据包含大量信息，可以大大改进识别文本中 GO 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，不仅捕获有关 GO 术语的语言的信息，还捕获该术语所指的生物医学实体的信息； (2) 使用 (1) 中制定的 GO 表示来分析酿酒酵母数据挖掘项目的结果，以确定哪些可能会产生改进的 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，看看新的 GO 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。该提案的数据挖掘工具可以扩展到 GO 以外的本体，从而充分利用本体开发上所花费的精力。