阐明语义-拓学权衡对基于知识推断的药理学发现的权衡
Daniel N Sosa1, Georgiana Neculae2, Julien Fauqueur2
1Stanford University, Department of Biomedical Data Science, Stanford, CA, USA.
Journal of biomedical semantics
|May 1, 2024
概括
药物发现中的人工智能 (AI) 可能会受到网络结构的偏见. 需要新的方法来利用生物医学知识图表来实现准确的药理创新.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
- 药理学发现 药理学发现
背景情况:
- 人工智能 (AI) 通过合成大量的生物医学知识,为加速药理学发现提供了巨大的潜力.
- 代表药物,疾病,基因和蛋白质之间的相互作用的知识图对于基于机器学习 (ML) 的发现方法,如链接预测至关重要.
- 现有的预测模型经常受到网络拓的影响,依赖于高度节点而不是细微的生物学理解.
研究的目的:
- 调查网络拓学对知识图中生物关系的语义理解的混效应.
- 评估拓偏差对药物重定向应用的性能的影响.
- 确定对新型知识表示和推断方法的需求,以实现有效的药理创新.
主要方法:
- 开发一个实验管道,在生物医学知识图表上进行语义和拓扰动.
- 在不同的拓条件下,当生物语义被废除时,对药物重用性能降低的评估.
- 量化由于拓偏差缓解而导致性能下降的增加.
主要成果:
- 废除有意义的语义导致药物重新利用的性能下降.
- 当拓偏差被减轻时,这种性能下降在两个不同的网络中分别增加了21%和38%.
- 拓学偏见严重影响了知识图中的生物学关系的解释.
结论:
- 目前生物医学知识图表中的知识表示和推断方法不足以充分利用生物语义.
- 解决拓偏差对于提高人工智能驱动的药理学发现的准确性和可靠性至关重要.
- 需要新的方法来开发强大的知识表示和推断方法,以推进药理创新.
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