Dobreva Jovana, Simjanoska Misheva Monika, Mishev Kostadin, Trajanov Dimitar, Mishkovski Igor
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia.
Brain Sci. 2025 May 19;15(5):523. doi: 10.3390/brainsci15050523.
This review paper synthesizes the application of knowledge graphs (KGs) in Alzheimer's disease (AD) research, based on two basic questions, as follows: what types of input data are available to construct these knowledge graphs, and what purpose the knowledge graph is intended to fulfill. We synthesize results from existing works to illustrate how diverse knowledge graph structures behave in different data availability settings with distinct application targets in AD research. By comparative analysis, we define the best methodology practices by data type (literature, structured databases, neuroimaging, and clinical records) and application of interest (drug repurposing, disease classification, mechanism discovery, and clinical decision support). From this analysis, we recommend AD-KG 2.0, which is a new framework that coalesces best practices into a unifying architecture with well-defined decision pathways for implementation. Our key contributions are as follows: (1) a dynamic adaptation mechanism that adapts methodological elements automatically according to both data availability and application objectives, (2) a specialized semantic alignment layer that harmonizes terminologies across biological scales, and (3) a multi-constraint optimization approach for knowledge graph building. The framework accommodates a variety of applications, including drug repurposing, patient stratification for precision medicine, disease progression modeling, and clinical decision support. Our system, with a decision tree structured and pipeline layered architecture, offers research precise directions on how to use knowledge graphs in AD research by aligning methodological choice decisions with respective data availability and application goals. We provide precise component designs and adaptation processes that deliver optimal performance across varying research and clinical settings. We conclude by addressing implementation challenges and future directions for translating knowledge graph technologies from research tool to clinical use, with a specific focus on interpretability, workflow integration, and regulatory matters.
本综述论文基于以下两个基本问题，综合了知识图谱（KGs）在阿尔茨海默病（AD）研究中的应用：构建这些知识图谱可获得哪些类型的输入数据，以及知识图谱旨在实现何种目的。我们综合现有研究成果，以说明在AD研究中，不同的知识图谱结构在不同的数据可用性设置及不同应用目标下的表现。通过比较分析，我们根据数据类型（文献、结构化数据库、神经影像和临床记录）以及感兴趣的应用（药物重新利用、疾病分类、机制发现和临床决策支持）定义了最佳方法实践。通过该分析，我们推荐了AD-KG 2.0，这是一个新框架，它将最佳实践整合到一个统一架构中，并具有明确的实施决策路径。我们的主要贡献如下：（1）一种动态适应机制，可根据数据可用性和应用目标自动调整方法元素；（2）一个专门的语义对齐层，可协调跨生物尺度的术语；（3）一种用于知识图谱构建的多约束优化方法。该框架适用于多种应用，包括药物重新利用、精准医学的患者分层、疾病进展建模和临床决策支持。我们的系统采用决策树结构和管道分层架构，通过将方法选择决策与各自的数据可用性和应用目标相结合，为AD研究中如何使用知识图谱提供了精确的方向。我们提供了精确的组件设计和适应过程，可在不同的研究和临床环境中实现最佳性能。我们通过解决将知识图谱技术从研究工具转化为临床应用的实施挑战和未来方向来结束本文，特别关注可解释性、工作流程整合和监管问题。
