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用人工智能辅助的数据收集和信心水平量化方法优化化学诱导胆固醇的不良结果途径网络

Jonas van Ertvelde¹, Anouk Verhoeven¹, Amy Maerten¹

¹Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium.

Journal of biomedical informatics

|August 4, 2023

概括

本研究介绍了一种人工智能驱动的方法,以优化化学诱导胆固醇的不良结果通路 (AOP) 网络. 人工智能方法自动化数据收集和信任评估,创建一个更具信息性的AOP网络来预测毒性.

科学领域:

毒理学和风险评估
计算毒理学计算毒理学
系统生物学系统生物学

背景情况:

不良结果路径 (AOP) 网络可视化毒性机制,但手动更新耗时且容易出现错误.
AOP网络需要经常更新新数据以保持相关性和全面性.
现有的AOP网络优化流程是手动的,这给效率和数据完整性带来了挑战.

研究的目的:

开发和实施人工智能 (AI) 辅助的方法来优化AOP网络.
为了自动化数据收集和量化评估对化学诱导胆固醇酶的AOP组件的信心.
提高AOP网络在毒理风险评估中的可靠性和信息性.

主要方法:

利用人工智能通过Sysrev平台进行自动数据收集.
使用定制的布拉德福德-希尔标准 (生物可信性,经验证据,基本性) 量化信心水平.
整合了信任评分,以建立关键事件关系的信任值,并使用Cytoscape可视化了网络.

主要成果:

在AOP网络中确定了38个独特的关键事件和135个关键事件关系.
载体变化成为发生率最高的关键事件,与胆固醇形成有很强的联系.
确定了核受体变化,胆酸积累,氧化应激,炎症和亡等关键事件.

结论:

使用人工智能创建了一个广泛信息化的AOP网络,用于使用化学诱导胆固醇的胆固醇.
优化的AOP网络作为开发体外测试的机制指南.
这种方法有助于可靠地预测化学诱导的胆固醇损伤.

关键词:

在AOP网络中,AOP网络是AOP网络.负面结果的途径.胆固醇的形成胆固醇的形成机械毒理学机械毒理学闪亮的应用程序闪亮的应用程序

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