ShanghaiTech University Knowledge Management System
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 |
DOI | 10.1101/2025.02.10.637419 |
相关网址 | 查看原文 |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 |
个性服务 |
查看访问统计 |
谷歌学术 |
谷歌学术中相似的文章 |
[Tao, Siyu]的文章 |
[Yang, Yang]的文章 |
[Liu, Xin]的文章 |
百度学术 |
百度学术中相似的文章 |
[Tao, Siyu]的文章 |
[Yang, Yang]的文章 |
[Liu, Xin]的文章 |
必应学术 |
必应学术中相似的文章 |
[Tao, Siyu]的文章 |
[Yang, Yang]的文章 |
[Liu, Xin]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
修改评论
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。