自然语言处理在毒理学中用于提取机械信息的应用
Marie Corradi1, Thomas Luechtefeld2,3, Alyanne M de Haan1
1Innovative Testing in Life Sciences and Chemistry, Utrecht University of Applied Sciences, Utrecht, Netherlands.
Frontiers in toxicology
|May 27, 2024
概括
自然语言处理 (NLP) 通过自动化对不良结果途径 (AOP) 的文献审查来加速毒理学研究. 这种方法提高了理解化合物诱导的不良影响的准确性和效率.
科学领域:
- 计算毒理学计算毒理学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 负面结果路径 (AOPs)
背景情况:
- 不良结果途径 (AOP) 对于理解毒性机制至关重要,但它们的构建是劳动密集型的.
- 用于AOP开发的手册文献审查是耗时的,主观的,并且阻碍了可重复性.
- 现有的方法缺乏用于毒理学研究的系统和快速信息提取.
研究的目的:
- 展示自然语言处理 (NLP) 如何自动化和增强不利结果路径 (AOP) 的构建.
- 开发和验证NLP管道,从科学文献中提取毒理学信息.
- 调查NLP在识别与肝脏不利状况相关的化合物的实用性,如胆固醇和胆固醇症.
主要方法:
- 利用深度学习语言模型用于命名实体识别 (NER) 来识别毒理学实体.
- 实施了基于规则的关系提取模型,以确定实体之间的因果关系.
- 将NLP管道应用于科学文献中,专注于肝脏不利状况 (胆固醇,胆固醇).
主要成果:
- 在NLP管道中,成功选了与肝脏不良相关的化合物.
- 从分子到生物水平的机械信息被提取出了不利结果的发展.
- 在现代毒理学和AOP开发中展示了NLP应用的概念验证.
结论:
- NLP显著促进了用于构建和优化不良结果途径 (AOPs) 的信息提取.
- 开发的开源NLP模型为识别毒理学实体及其关系提供了一个模板.
- 这种方法提高了毒理学研究的效率,准确性和可重复性.
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