LIDDI - Provenance-Centered Dataset of Drug-Drug Interactions

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.

多年来，多项研究已证实，可通过从文献（MEDLINE）、电子健康记录、公共数据库（Drugbank，药物银行数据库）等资源中开展数据挖掘，识别潜在的药物-药物相互作用。尽管上述各类方法均经过了严谨的统计学验证，但均未将不同方法间的重叠情况作为决策变量之一纳入考量范畴。本文中我们提出了链接式药物-药物相互作用数据集（LInked Drug-Drug Interactions, LIDDI）——这是一款基于纳米出版物（nanopublication）的公开资源描述框架（RDF）数据集，搭载可靠的统一资源标识符（URIs）。该数据集囊括了当前被引用最为广泛的若干预测方法与数据源，可为研究人员提供将他人研究成果复用至自身预测方法的资源支持。由于使用外部资源的核心痛点之一，在于药物名称与所用标识符之间的映射不统一，因此我们还提供了一套精心整理的映射集，以便对本数据集所整合的多类数据源开展比对分析。