MuCoS：通过知识图谱中的多上下文感知采样实现高效药物靶点发现
MuCoS: Efficient Drug Target Discovery via Multi Context Aware Sampling in Knowledge Graphs
AI总结：
MuCoS提出多上下文感知采样框架，通过融合知识图谱高密度邻居结构模式与BERT文本嵌入，免去负采样并提升药物靶点链接预测效率与精度。
摘要：
药物靶点相互作用的准确预测对于加速药物发现和阐明复杂生物机制至关重要。在这项工作中，我们将药物靶点预测构建为异构生物医学知识图谱（KG）上的链接预测任务，该知识图谱整合了药物、蛋白质、疾病、通路和其他相关实体。传统的知识图谱嵌入方法（如TransE和ComplEx SE）受到其对计算密集型负采样的依赖以及对未见药物靶点对的有限泛化能力的制约。为了解决这些挑战，我们提出了多上下文感知采样（MuCoS），这是一个新颖的框架，它优先考虑高密度邻居以捕获显著的结构模式，并将这些模式与从BERT派生的上下文嵌入相整合。通过统一结构和文本模态，并选择性地采样高信息量的模式，MuCoS避免了负采样的需要，显著降低了计算开销，同时提高了对新型药物靶点关联和药物靶点的预测准确性。在KEGG50k数据集上的大量实验表明，MuCoS优于最先进的基线方法，在预测数据集中任意关系时平均倒数排名（MRR）提高了高达13%，在专门的药物靶点关系预测中提高了6%。
英文摘要：
Accurate prediction of drug target interactions is critical for accelerating drug discovery and elucidating complex biological mechanisms. In this work, we frame drug target prediction as a link prediction task on heterogeneous biomedical knowledge graphs (KG) that integrate drugs, proteins, diseases, pathways, and other relevant entities. Conventional KG embedding methods such as TransE and ComplEx SE are hindered by their reliance on computationally intensive negative sampling and their limited generalization to unseen drug target pairs. To address these challenges, we propose Multi Context Aware Sampling (MuCoS), a novel framework that prioritizes high-density neighbours to capture salient structural patterns and integrates these with contextual embeddings derived from BERT. By unifying structural and textual modalities and selectively sampling highly informative patterns, MuCoS circumvents the need for negative sampling, significantly reducing computational overhead while enhancing predictive accuracy for novel drug target associations and drug targets. Extensive experiments on the KEGG50k dataset demonstrate that MuCoS outperforms state-of-the-art baselines, achieving up to a 13\% improvement in mean reciprocal rank (MRR) in predicting any relation in the dataset and a 6\% improvement in dedicated drug target relation prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
