SIDEKICK: 一种集成语义的药物效应、适应症和禁忌症资源
SIDEKICK: A Semantically Integrated Resource for Drug Effects, Indications, and Contraindications
AI总结：
SIDEKICK通过语义集成药物效应、适应症和禁忌症，提升药物再利用中的副作用相似性分析能力。
摘要：
药物警报和临床决策支持系统利用结构化的药物安全数据来指导医疗实践。然而，现有数据集经常依赖于如MedDRA等术语，这限制了它们的语义推理能力和与语义网络和知识图谱的互操作性。为了解决这一差距，我们开发了SIDEKICK，一个知识图谱，它从FDA结构化产品标签中标准化药物适应症、禁忌症和不良反应。我们开发并使用了一种基于大型语言模型（LLM）提取和图检索增强生成（图RAG）的工作流程进行本体映射。我们处理了超过50,000个药物标签，并将术语映射到人类表型本体（HPO）、MONDO疾病本体和RxNorm。我们的语义集成资源在药物再利用任务中，通过副作用相似性优于SIDER和ONSIDES数据库。我们将数据集序列化为资源描述框架（RDF）图，并使用Semanticscience集成本体（SIO）作为上层本体以进一步提高互操作性。因此，SIDEKICK能够实现自动化安全监视和基于表型的相似性分析用于药物再利用。
英文摘要：
Pharmacovigilance and clinical decision support systems utilize structured drug safety data to guide medical practice. However, existing datasets frequently depend on terminologies such as MedDRA, which limits their semantic reasoning capabilities and their interoperability with Semantic Web ontologies and knowledge graphs. To address this gap, we developed SIDEKICK, a knowledge graph that standardizes drug indications, contraindications, and adverse reactions from FDA Structured Product Labels. We developed and used a workflow based on Large Language Model (LLM) extraction and Graph-Retrieval Augmented Generation (Graph RAG) for ontology mapping. We processed over 50,000 drug labels and mapped terms to the Human Phenotype Ontology (HPO), the MONDO Disease Ontology, and RxNorm. Our semantically integrated resource outperforms the SIDER and ONSIDES databases when applied to the task of drug repurposing by side effect similarity. We serialized the dataset as a Resource Description Framework (RDF) graph and employed the Semanticscience Integrated Ontology (SIO) as upper level ontology to further improve interoperability. Consequently, SIDEKICK enables automated safety surveillance and phenotype-based similarity analysis for drug repurposing.
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。
