闪亮TPs:从文本挖掘结果中整理转换产品
Emma H Palm1, Parviel Chirsir1, Jessy Krier1
1Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg.
概括
ShinyTPs通过从PubChem.com整理化学反应来改善对转化产品 (TP) 数据的访问. 这种FAIRification有助于识别TP,增强环境和人类健康风险评估.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 转化产品 (TP) 数据对于评估化学危险至关重要,但通常是有限的,而不是 FAIR (可查找,可访问,可互操作,可重复使用).
- 改善TP的数据可访问性可以支持识别工作流程和机器学习应用程序.
研究的目的:
- 开发ShinyTPs,这是一个R包,用于从文本挖掘的PubChem数据中策划转换信息.
- 提高现有的TP知识的公平性,并促进TP识别.
主要方法:
- ShinyTPs是为了整理和可视化从PubChem.com中提取的文字化学反应数据而开发的.
- 该应用程序被用于优先考虑对436种暂定识别的化合物进行TP检索.
- 在PubChem Transformations库中添加了精选的反应,用于patRoon工作流.
主要成果:
- 闪亮TPs提取了645种反应 (496种化合物),其中319种是PubChem.的新反应.
- 策划的图书馆有助于初步识别72种化合物.
- 18%的已识别的化合物使用ShinyTPs进行了策划,证明了它的实用性.
结论:
- 闪亮TPs有效地策划和公平地提供TP信息,改善数据可访问性.
- 该应用程序支持非目标分析中的TP识别,有助于更好的风险评估.
- 公平的TP数据是迈向强有力的化学安全评估的实际步骤.
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