Stanford University, Department of Biomedical Data Science, Stanford, CA, USA.
BenevolentAI, London, UK.
J Biomed Semantics. 2024 May 1;15(1):5. doi: 10.1186/s13326-024-00308-z.
Leveraging AI for synthesizing the deluge of biomedical knowledge has great potential for pharmacological discovery with applications including developing new therapeutics for untreated diseases and repurposing drugs as emergent pandemic treatments. Creating knowledge graph representations of interacting drugs, diseases, genes, and proteins enables discovery via embedding-based ML approaches and link prediction. Previously, it has been shown that these predictive methods are susceptible to biases from network structure, namely that they are driven not by discovering nuanced biological understanding of mechanisms, but based on high-degree hub nodes. In this work, we study the confounding effect of network topology on biological relation semantics by creating an experimental pipeline of knowledge graph semantic and topological perturbations. We show that the drop in drug repurposing performance from ablating meaningful semantics increases by 21% and 38% when mitigating topological bias in two networks. We demonstrate that new methods for representing knowledge and inferring new knowledge must be developed for making use of biomedical semantics for pharmacological innovation, and we suggest fruitful avenues for their development.
利用人工智能合成海量生物医学知识，对于药物发现具有巨大的潜力，其应用包括为未治疗的疾病开发新的疗法，以及将药物重新用于新兴的大流行病治疗。通过基于嵌入的机器学习方法和链接预测，创建相互作用的药物、疾病、基因和蛋白质的知识图表示，从而实现发现。以前已经表明，这些预测方法容易受到网络结构偏差的影响，也就是说，它们不是通过发现细微的机制生物学理解，而是基于高度数的中心节点来驱动的。在这项工作中，我们通过创建知识图语义和拓扑扰动的实验管道，研究网络拓扑对生物关系语义的混淆效应。我们表明，在两个网络中减轻拓扑偏差时，从消除有意义的语义中药物重新定位性能下降了 21%和 38%。我们证明，必须开发新的知识表示和推理新知识的方法，以便将生物医学语义用于药物创新，并为它们的发展提出了富有成效的途径。
