系统梳理生物医学知识图谱的领域、任务与应用,揭示其在精准医疗中的价值。
Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications
- 从分子互作、药物数据等多源信息构建知识图谱
- 支持知识管理、检索、推理与解释等核心任务
- 助力药物研发与精准医疗,适合研究者与临床人员参考
生物医学知识图谱(BKGs)已成为组织和利用生物医学领域海量复杂数据的强大工具。现有综述多局限于特定领域或方法,未能涵盖整体发展态势与技术革新。本文从三个核心视角系统综述BKGs:领域、任务与应用。首先分析其如何融合分子互作、药理学数据与临床记录等多元来源构建;其次探讨知识管理、检索、推理与解释等关键任务;最后展示其在精准医疗、药物发现与科研中的实际应用,凸显跨领域转化潜力。通过整合三方面视角,本综述不仅厘清当前研究现状,也为未来方法创新与落地实践奠定基础。
原文摘要 · Abstract (English)
Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods, overlooking the broader landscape and the rapid technological progress reshaping it. In this survey, we address this gap by offering a systematic review of BKGs from three core perspectives: domains, tasks, and applications. We begin by examining how BKGs are constructed from diverse data sources, including molecular interactions, pharmacological datasets, and clinical records. Next, we discuss the essential tasks enabled by BKGs, focusing on knowledge management, retrieval, reasoning, and interpretation. Finally, we highlight real-world applications in precision medicine, drug discovery, and scientific research, illustrating the translational impact of BKGs across multiple sectors. By synthesizing these perspectives into a unified framework, this survey not only clarifies the current state of BKG research but also establishes a foundation for future exploration, enabling both innovative methodological advances and practical implementations.
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