使用启发式模拟和图形数据科学探索对不利结果途径的遗传影响
Joseph D Romano1,2,3, Liang Mei4, Jonathan Senn4
1Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Computational toxicology (Amsterdam, Netherlands)
|October 13, 2023
概括
人工智能和不良结果途径揭示了肝癌的新遗传风险因素. 这项研究确定了AHR和ABCB11中的新型基因变异作为毒性中介性肝癌的潜在贡献者.
科学领域:
- 毒理学 毒理学 毒理学
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 有害结局途径 (AOPs) 阐明了毒性诱导疾病中的生物信号.
- 了解毒性中介性肝癌的遗传机制具有临床意义.
- 现有的AOP框架可以用AI增强,以获得新的见解.
研究的目的:
- 将AOP框架与AI方法整合起来,以发现与毒性相关的肝癌的遗传驱动因素.
- 通过AOP数据和现实世界遗传信息的结合,识别肝癌的新型遗传风险因素.
- 将生成和图形机器学习应用于AOP和遗传数据集.
主要方法:
- 利用不良结果路径数据库 (AOP-DB) 进行疾病特定的AOP和图形神经网络构建.
- 雇佣的英国生物库遗传数据 (SNP数据) 和表型队列 (肝癌病例/对照).
- 应用自动化机器学习,遗传算法和图形机器学习,用于共变量平衡的倾向性得分匹配.
主要成果:
- 开发了一种结合AOP和AI用于毒性遗传学研究的新方法.
- 确定了一种新的肝癌潜在风险因素,涉及基因受体 (AHR) 和ATP结合盒子亚家族B成员11 (ABCB11) 基因的遗传变异.
- 成功整合了各种数据来源,包括AOP-DB和英国生物银行.
结论:
- 联合的AOP和AI框架为毒性中介的不良健康结果提供了强大的洞察力.
- 在AHR和ABCB11中的遗传变异代表了肝癌的新潜在风险因素.
- 这项研究为更精确地了解和预测化学诱导疾病铺平了道路.
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