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    This study introduces Temporal Contrastive Learning (TCL) to generate biomedical hypotheses by modeling the co-evolution of scientific terms across multiple temporal knowledge bases, improving research discovery.

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    Area of Science:

    • Biomedical Informatics
    • Computational Biology
    • Artificial Intelligence in Medicine

    Background:

    • Hypothesis Generation (HG) accelerates biomedical research by extracting novel insights from scientific literature.
    • Existing HG methods often overlook the temporal dynamics of scientific terms, limiting their ability to capture evolving knowledge.
    • Multi-source temporal knowledge bases (KBs) contain crucial up-to-date information that is underutilized in current HG approaches.

    Purpose of the Study:

    • To develop an innovative Temporal Contrastive Learning (TCL) framework for Hypothesis Generation.
    • To effectively model the co-evolution of entities across multiple temporal KBs for uncovering latent associations.
    • To enhance the temporal evolutional embeddings of scientific terms by integrating multi-source temporal information.

    Main Methods:

    • Constructed a temporal relation graph using PubMed papers and the Comparative Toxicogenomics Database (CTD).
    • Utilized a temporal concept graph from Medical Subject Headings (MeSH) alongside the temporal relation graph.
    • Trained two Graph Convolutional Network (GCN)-based recurrent networks to learn entity temporal evolutional embeddings.
    • Implemented a cross-view temporal prediction task for learning knowledge-enriched temporal embeddings via contrasting embeddings from two Temporal Knowledge Graphs (TKGs).

    Main Results:

    • The proposed TCL framework demonstrated superior performance compared to single TKG-based approaches.
    • Achieved state-of-the-art results on three real-world biomedical term relationship datasets.
    • Effectively captured and modeled the temporal evolution of scientific terms and their associations.

    Conclusions:

    • The TCL framework successfully integrates multi-source temporal KB information for Hypothesis Generation.
    • Jointly modeling entity co-evolution across temporal KBs enhances the discovery of biomedical relationships.
    • This approach offers a significant advancement in leveraging dynamic scientific knowledge for hypothesis generation.