将PubTator的AI驱动文本挖掘集成到比较毒基因组学数据库的手动策划工作流程中
Thomas C Wiegers1, Allan Peter Davis1, Jolene Wiegers1
1Department of Biological Sciences, North Carolina State University, Toxicology Building, 850 Main Campus Drive, Raleigh, NC 27695, USA.
Database : the journal of biological databases and curation
|February 21, 2025
概括
比较毒基因组学数据库 (CTD) 现在使用PubTator 3.0,一种AI工具,以加快科学文献关于环境暴露和人类健康的策划. 这种整合提高了连接化学物质,基因,疾病和表型的效率.
科学领域:
- 毒素基因组学 毒素基因组学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 比较毒基因组学数据库 (CTD) 手动整理文献以了解环境暴露和人类健康.
- CTD使用本体学和受控词汇组织化学物质,基因,表型,疾病和物种之间的分子关系.
- 手动的文本规范化用于策划是耗时的.
研究的目的:
- 为了提高CTD文献策划流程的效率.
- 评估和整合PubTator 3.0,一个人工智能驱动的资源,进入CTD的工作流.
- 利用自然语言处理 (NLP) 来实现更快的数据提取和规范化.
主要方法:
- CTD评估了PubTator 3.0的功能与其特定的策划目标相比.
- PubTator 3.0 集成到 CTD 现有的策划工作流程中.
- 该研究描述了CTD与NLP的历史及其对NLP整合的战略.
主要成果:
- PubTator 3.0成功地提取和规范化了与CTD治疗需求相关的关键生物医学概念.
- 预计PubTator 3.0的集成将显著提高CTD生物库拉的效率.
- 这种人工智能驱动的方法可以更快地产生关于环境健康影响的假设.
结论:
- 整合像PubTator 3.0这样的AI工具可以提高大规模生物数据库的效率.
- 精简的治愈过程加速了毒基因组学领域的科学发现.
- CTD采用NLP表明了通过高效的数据管理来推进人类健康研究的承诺.
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