Kamdar Maulik R, Musen Mark A
Center for Biomedical Informatics Research, Stanford University, USA.
Proc Int World Wide Web Conf. 2017 Apr;2017:321-329. doi: 10.1145/3038912.3052692.
Integrated approaches for pharmacology are required for the mechanism-based predictions of adverse drug reactions that manifest due to concomitant intake of multiple drugs. These approaches require the integration and analysis of biomedical data and knowledge from multiple, heterogeneous sources with varying schemas, entity notations, and formats. To tackle these integrative challenges, the Semantic Web community has published and linked several datasets in the Life Sciences Linked Open Data (LSLOD) cloud using established W3C standards. We present the PhLeGrA platform for Linked Graph Analytics in Pharmacology in this paper. Through query federation, we integrate four sources from the LSLOD cloud and extract a drug-reaction network, composed of distinct entities. We represent this graph as a hidden conditional random field (HCRF), a discriminative latent variable model that is used for structured output predictions. We calculate the underlying probability distributions in the drug-reaction HCRF using the datasets from the U.S. Food and Drug Administration's Adverse Event Reporting System. We predict the occurrence of 146 adverse reactions due to multiple drug intake with an AUROC statistic greater than 0.75. The PhLeGrA platform can be extended to incorporate other sources published using Semantic Web technologies, as well as to discover other types of pharmacological associations.
对于因同时服用多种药物而出现的药物不良反应进行基于机制的预测，需要采用综合药理学方法。这些方法需要整合和分析来自多个异构源的生物医学数据和知识，这些源具有不同的模式、实体表示法和格式。为应对这些整合挑战，语义网社区已使用既定的W3C标准在生命科学链接开放数据（LSLOD）云中发布并链接了多个数据集。在本文中，我们展示了用于药理学链接图分析的PhLeGrA平台。通过查询联邦，我们整合了LSLOD云中的四个源，并提取了一个由不同实体组成的药物反应网络。我们将此图表示为隐藏条件随机字段（HCRF），这是一种用于结构化输出预测的判别性潜在变量模型。我们使用美国食品药品监督管理局不良事件报告系统的数据集来计算药物反应HCRF中的潜在概率分布。我们预测了因多种药物摄入导致的146种不良反应的发生，其受试者工作特征曲线下面积（AUROC）统计值大于0.75。PhLeGrA平台可以扩展以纳入使用语义网技术发布的其他源，以及发现其他类型的药理学关联。
