结构性出生缺陷的毒理学知识图
John Erol Evangelista1, Daniel J B Clarke1, Zhuorui Xie1
1Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Communications medicine
|July 17, 2023
概括
一个新的知识图,ReproTox-KG,通过分析药物和基因与出生缺陷的关联来识别潜在的原体. 该资源有助于预测临床前小分子带来的风险,并了解分子机制.
科学领域:
- 生物医学信息学 生物医学信息学
- 毒理学 毒理学 毒理学
- 遗传学 是一个遗传学.
背景情况:
- 出生缺陷影响美国约33分之一的出生,许多出生原因不明.
- 潜在的促成因素包括遗传因素,环境污染物和怀孕期间的药物暴露.
研究的目的:
- 开发一个全面的知识图 (ReproTox-KG) 来描述小分子,基因和先天缺陷之间的关联.
- 为了确定药物诱导的出生缺陷背后的分子机制.
主要方法:
- 通过整合有关药物/出生缺陷的数据,基因/出生缺陷的关联,基因表达变化,药物标,遗传负担得分和胎盘交叉的数据,构建了ReproTox-KG.
- 应用半监督学习 (SSL) 来评分临床前小分子的毒性潜力.
主要成果:
- 获得了超过30,000个临床前的小分子,用于胎盘交叉和出生缺陷诱导潜力.
- 确定了500多个出生缺陷/基因/药物集团,以阐明药物诱导的出生缺陷机制.
- 为ReproTox-KG推出了一个基于Web的界面:https://maayanlab.cloud/reprotox-kg.
结论:
- ReproTox-KG是了解出生缺陷分子机制的宝贵资源.
- 该平台可以预测基因和临床前小分子的原性潜力.
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