AOP-helpFinder 3.0:从文本挖掘到关键事件关系的网络可视化,以及来自多个来源的知识集成
Thomas Jaylet1, Florence Jornod1, Quentin Capdet1
1Université Paris Cité, Inserm, HealthFex, Paris F-75006, France.
Bioinformatics (Oxford, England)
|June 28, 2025
概括
该AOP-helpFinder 3.0工具通过整合各种数据源来绘制不良结果路径 (AOPs) 的图表来增强毒理学研究. 这有助于更深入地了解化学毒性机制,并支持预测毒理学.
科学领域:
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 不良结果途径 (AOP) 框架通过详细介绍从分子事件到不良结果的机制来规范毒理学研究.
- 整合庞大而复杂的毒理学数据仍然是一个重大挑战.
研究的目的:
- 加强AOP-helpFinder工具,以便更有效地分析和整合毒理学数据.
- 改进识别压力因素-事件和事件-事件关系,用于AOP开发.
主要方法:
- 利用文本挖掘,自然语言处理和基于图形的方法来分析科学文献.
- 在系统生物学背景下整合了额外的数据源和毒理学数据库信息.
- 开发了一个增强的网络服务器,具有交互式网络可视化功能.
主要成果:
- 该AOP-helpFinder 3.0工具自动选PubMed文献,以确定AOP构建的关键关系.
- 新的功能包括对毒理学数据库信息的注释和交互式网络可视化.
- 通过对暴露毒理学的案例研究来证明该工具的实用性.
结论:
- AOP-helpFinder 3.0为综合性和预测性毒理学提供了一个强大的解决方案.
- 这种增强的工具有助于更全面地了解毒理机制,并支持AOP的开发.
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