斯坦迪格姆ASKTM:知识图和人工智能平台应用于异常性肺纤维化患者的目标发现
Seokjin Han1, Ji Eun Lee2, Seolhee Kang1
1Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea.
Briefings in bioinformatics
|February 13, 2024
概括
斯坦迪格姆ASKTM使用知识图和AI识别了异形性肺纤维化 (IPF) 的新型治疗点. 这个平台验证了AMFR,MDFIC和NR5A2作为IPF治疗的潜在药物标.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 确定有效的药物点对于成功的药物开发至关重要.
- 异形性肺纤维化 (IPF) 由于其复杂的疾病机制而构成重大挑战.
- 现有的目标识别方法可能耗时且缺乏可解释性.
研究的目的:
- 推出Standigm ASKTM,这是一个人工智能驱动的平台,用于识别疾病机制中关键目标基因.
- 应用Standigm ASKTM来发现和验证IPF的潜在治疗点.
- 证明平台能够提供可解释的证据来识别目标.
主要方法:
- 将异质知识图 (KG) 数据库与基于注意力的神经网络模型集成.
- 使用交互式界面来探索AI预测的结果.
- 进行体外实验,包括TGFβ治疗和基因淘汰,以验证已识别的目标.
主要成果:
- 通过KG证据,Standigm ASKTM确定了AMFR,MDFIC和NR5A2作为与IPF相关的关键基因.
- 在体外,TGFβ治疗诱导的基因表达变化与表皮细胞-介质细胞过渡有关.
- 对AMFR,MDFIC和NR5A2的基因淘汰扭转了这些变化,证实了它们的相关性.
结论:
- Standigm ASKTM是一个创新的平台,将KG和AI结合起来,用于药物标发现.
- AMFR,MDFIC和NR5A2已被验证为IPF的潜在治疗标.
- 该平台提供可解释的子图证据,加速对疾病机制和药物开发的洞察.
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