Learning universal knowledge graph embedding for predicting biomedical pairwise interactions
2025-02-16
状态已发表
摘要

Predicting biomedical interactions is crucial for understanding various biological processes and drug discovery. Graph neural networks (GNNs) are promising in identifying novel interactions when extensive labeled data are available. However, labeling biomedical interactions is often time-consuming and labor-intensive, resulting in low-data scenarios. Furthermore, distribution shifts between training and test data in real-world applications pose a challenge to the generalizability of GNN models. Recent studies suggest that pre-training GNN models with self-supervised learning on unlabeled data can enhance their performance in predicting biomedical interactions. Here, we propose LukePi, a novel self-supervised pre-training framework that pre-trains GNN models on biomedical knowledge graphs (BKGs). LukePi is trained with two self-supervised tasks: topology-based node degree classification and semantics-based edge recovery. The former is to predict the degree of a node from its topological context and the latter is to infer both type and existence of a candidate edge by learning semantic information in the BKG. By integrating the two complementary tasks, LukePi effectively captures the rich information from the BKG, thereby enhancing the quality of node representations. We evaluate the performance of LukePi on two critical link prediction tasks: predicting synthetic lethality and drug-target interactions, using four benchmark datasets. In both distribution-shift and low-data scenarios, LukePi significantly outperforms 15 baseline models, demonstrating the power of the graph pre-training strategy when labeled data are sparse.

语种英语
DOI10.1101/2025.02.10.637419
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出处bioRxiv
收录类别PPRN.PPRN
WOS记录号PPRN:121524849
WOS类目Computer Science, Interdisciplinary Applications
文献类型预印本
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/514098
专题信息科学与技术学院
信息科学与技术学院_博士生
信息科学与技术学院_PI研究组_郑杰组
通讯作者Tao, Siyu
作者单位
1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
2.Lingang Lab, Shanghai, Peoples R China
3.Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China
推荐引用方式
GB/T 7714
Tao, Siyu,Yang, Yang,Liu, Xin,et al. Learning universal knowledge graph embedding for predicting biomedical pairwise interactions. 2025.
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