理解生物医学网络中复杂的相互作用对于生物医学的进步至关重要，但传统的链路预测（LP）方法在捕捉这种复杂性方面受到限制。基于表示的学习技术通过将节点映射到低维嵌入来提高预测准确性，但它们经常在可解释性和可扩展性方面遇到困难。我们提出了 BioPathNet，这是一种基于神经贝尔曼-福特网络 (NBFNet) 的新型图神经网络框架，通过生物医学知识图中的 LP 的基于路径的推理来解决这些局限性。与节点嵌入框架不同，BioPathNet 通过考虑路径上的所有关系来学习节点对之间的表示，从而提高预测准确性和可解释性。这使得有影响力的路径可视化并促进生物学验证。 BioPathNet 利用背景调节图 (BRG) 来增强消息传递，并使用严格的负采样来提高精度。在各种 LP 任务的评估中，例如基因功能注释、药物疾病指示、合成致死率和 lncRNA-mRNA 相互作用预测，BioPathNet 始终优于浅层节点嵌入方法、关系图神经网络和特定于任务的状态艺术方法，展示了强大的性能和多功能性。我们的研究预测了治疗急性淋巴细胞白血病 (ALL) 和阿尔茨海默病等疾病的新药物适应症，并经过医学专家和临床试验的验证。我们还确定了新的合成致死基因对以及涉及 lncRNA 和靶基因的调控相互作用，并通过文献综述得到证实。 BioPathNet 的可解释性将使研究人员能够追踪预测路径并获得分子见解，使其成为药物发现、个性化医疗和生物学的宝贵工具。

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.