计算机科学
药物重新定位
重新调整用途
机器学习
人工智能
图形
任务(项目管理)
数据挖掘
集合(抽象数据类型)
药品
理论计算机科学
精神科
生物
经济
管理
程序设计语言
生态学
心理学
作者
Chenglin Yang,Xianlai Chen,Jincai Huang,Ying An,Zhenyu Huang,Yu Sun
标识
DOI:10.1016/j.compbiomed.2024.107936
摘要
Drug repurposing is a strategy aiming at uncovering novel medical indications of approved drugs. This process of discovery can be effectively represented as a link prediction task within a medical knowledge graph by predicting the missing relation between the disease entity and the drug entity. Typically, the links to be predicted pertain to rare types, thereby necessitating the task of few-shot link prediction. However, the sparsity of neighborhood information and weak triplet interactions result in less effective representations, which brings great challenges to the few-shot link prediction. Therefore, in this paper, we proposed a meta-learning framework based on a multi-level attention network (MLAN) to capture valuable information in the few-shot scenario for drug repurposing. First, the proposed method utilized a gating mechanism and a graph attention network to effectively filter noise information and highlight the valuable neighborhood information, respectively. Second, the proposed commonality relation learner, employing a set transformer, effectively captured triplet-level interactions while remaining insensitive to the size of the support set. Finally, a model-agnostic meta-learning training strategy was employed to optimize the model quickly on each meta task. We conducted validation of the proposed method on two datasets specifically designed for few-shot link prediction in medical field: COVID19-One and BIOKG-One. Experimental results showed that the proposed model had significant advantages over state-of-the-art few-shot link prediction methods. Results also highlighted the valuable insights of the proposed method, which successfully integrated the components within a unified meta-learning framework for drug repurposing.
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