毒素测试 (ToxAssay):一种基于层次模型的工具,用于先进的毒素基因组学生物标志物发现
Md Masud Rana1,2, Md Nurul Haque Mollah3, Mohammed H Albujja4
1National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
Bioinformatics (Oxford, England)
|October 11, 2025
概括
一个新的R包,ToxAssay,使用层次线性模型 (HLM) 来改善药物诱导毒性生物标志物的识别. 它在分析复杂的毒基因组学数据时提供了更高的准确性和效率.
科学领域:
- 毒素基因组学 毒素基因组学
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 了解药物诱导的毒性对于安全药物开发至关重要.
- 在中毒基因组学分析有助于早期检测毒性生物标志物.
- 现有的工具面临着复杂的可变相互依赖的挑战,阻碍了准确的生物标志物识别.
研究的目的:
- 开发和实施一个新的层次线性模型 (HLM) 进行全面的毒性评估.
- 创建一个R包,ToxAssay,以解决当前毒基因组学分析工具的局限性.
主要方法:
- 一个等级线性模型 (HLM) 的开发.
- 在开源R包ToxAssay中实现HLM.
- 毒素测试对谷氨耗尽诱导的毒性数据的应用.
主要成果:
- 与现有方法相比,ToxAssay证明了优越的生物标志物检测和计算效率.
- 鉴定了71个关键基因和26个核心基因,具有高分辨准确度 (AUC=0.97),用于谷氨耗尽毒性.
- 先进的结果途径 (AOP) 分析揭示了与谷氨耗尽相关的疾病结果,提供了分子机制的洞察力.
结论:
- 毒素检测有效地识别毒性生物标志物,并阐明药物诱导毒性的分子机制.
- 该R包为分析复杂的毒基因组学数据集提供了强大的解决方案.
- 结果提供了精确的洞察力谷氨耗尽诱导的毒性和潜在的疾病结果.
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