Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses from existing scientific literature. Most existing studies focused on modeling static snapshots of the corpus, neglecting the temporal evolution of scientific terms. Despite recent efforts to learn term evolution from Knowledge Bases (KBs) for HG, the temporal information from multi-source KBs is still overlooked, which contains important, up-to-date knowledge. In this paper, an innovative Temporal Contrastive Learning (TCL) framework is introduced to uncover latent associations between entities by jointly modeling their co-evolution across multi-source temporal KBs. Specifically, we first construct a temporal relation graph based on PubMed papers and a biomedical relation database (such as Comparative Toxicogenomics Database (CTD)). Then the constructed temporal relation graph and a temporal concept graph (such as Medical Subject Headings (MeSH)) are used to train two GCN-based recurrent networks for learning the entity temporal evolutional embeddings, respectively. Finally, a cross-view temporal prediction task is designed for learning knowledge enriched temporal embeddings by contrasting the temporal embeddings learned from the two Temporal Knowledge Graphs (TKGs). Findings from experiments conducted on three real-world biomedical term relationship datasets demonstrate that the proposed approach is clearly superior to approaches based on single TKG, achieving the state-of-the-art performance.

Huiwei Zhou

Wenchu Li

Weihong Yao

Yingyu Lin

Lei Du

IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 强调算法、数学、统计和计算方法是生物信息学和计算生物学的核心；开发和测试有效的生物信息学计算机程序；生物数据库的开发；以及使用这些方法、程序和数据库获得的重要生物学结果；系统生物学的新兴领域，其中使用多种形式的数据来创建复杂生物系统的基于计算机的模型

IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the central role of algorithms, mathematics, statistics, and computational methods in bioinformatics and computational biology. The journal focuses on the development and testing of effective bioinformatics software, the construction of biological databases, and the significant biological findings obtained through these methods, programs, and databases. Additionally, it covers the emerging field of systems biology, which explores how to create computational models of complex biological systems using various forms of data.

IEEE/ACM Transactions on Computational Biology and Bioinformatics 强调算法、数学、统计和计算方法在生物信息学和计算生物学中的核心作用。期刊关注有效的生物信息学计算程序的开发与测试，生物数据库的构建，以及利用这些方法、程序和数据库所获得的重要生物学成果。此外，期刊还涵盖系统生物学的新兴领域，探讨如何使用多种数据形式构建复杂生物系统的计算模型。

Category	Quartile
生物学	3
生物学, 生化研究方法	3
生物学, 数学跨学科应用	3
生物学, 统计学与概率论	3
生物学, 计算机跨学科应用	4

Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation

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Journal Info

Category	Quartile
STATISTICS & PROBABILITY	1