计算机科学
药物重新定位
工作流程
药品
疾病
稳健性(进化)
机器学习
图形
人工智能
数据挖掘
计算生物学
医学
基因
理论计算机科学
生物
药理学
生物化学
病理
数据库
作者
Yinbo Liu,Grigor'eva NIu,Siqi Wu,Jinming Wang,Hesong Qiu,Sijun Meng,Wen Zhang
标识
DOI:10.1101/2024.12.30.630746
摘要
ZGNT, an innovative, novel workflow for zero-shot learning in drug repurposing that leverages meta-path graph neural network transform- ers. This method infers disease-drug relationships indirectly, utilizing disease-gene associations and gene-drug interactions via acting genes. It also generates a TOP drug-TOP gene linkage map, providing clini- cians with a visual tool to assess the plausibility of the suggested drugs before advancing to clinical trials. Experimental results demonstrate that, among the top 1% of disease-related drug candidates identified by ZGNT, the proportion of usable drugs is ***, surpassing four base- line methods. Furthermore, the accuracy of interaction link prediction achieves ***, also surpassing these benchmarks. Ultimatley, by inte- grating data acquisition, model design, and application workflows, our approach effectively identifies potential drug targets, offering new in- sights into therapeutic strategies for disease management. This paper thoroughly details the methodology and validates its effectiveness across various diseases, highlighting its robustness and practical value.
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