Big Data Institute, Central South University, Changsha, 410083, China; School of Life Sciences, Central South University, Changsha, 410083, China.
Big Data Institute, Central South University, Changsha, 410083, China.
Comput Biol Med. 2024 Mar;170:107936. doi: 10.1016/j.compbiomed.2024.107936. Epub 2024 Jan 6.
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.
药物重定位是一种旨在发现已批准药物新的医学用途的策略。通过预测疾病实体和药物实体之间缺失的关系，可以有效地将这个发现过程表示为医学知识图中的链接预测任务。通常，要预测的链接涉及罕见类型，因此需要进行少量样本链接预测。然而，由于邻居信息的稀疏性和三元组交互作用的减弱，导致表示效果较差，这给少量样本链接预测带来了很大的挑战。因此，在本文中，我们提出了一种基于多层次注意力网络（MLAN）的元学习框架，用于在药物重定位的少量样本场景中捕获有价值的信息。首先，该方法利用门控机制和图注意力网络，有效地过滤噪声信息并突出有价值的邻居信息。其次，所提出的共性关系学习器采用集合转换器，有效地捕获了三元组级别的交互作用，同时对支持集的大小不敏感。最后，采用模型不可知的元学习训练策略在每个元任务上快速优化模型。我们在两个专门为医学领域少量样本链接预测设计的数据集 COVID19-One 和 BIOKG-One 上验证了所提出的方法。实验结果表明，所提出的模型在少量样本链接预测方法方面具有显著优势。结果还突出了所提出方法的有价值的见解，该方法成功地将组件集成到药物重定位的统一元学习框架中。
