PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data.
PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data.
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DOI：
10.1145/3038912.3052692
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Musen MA
中科院分区：
文献类型：
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作者：
Kamdar MR;Musen MA
药理学的综合方法需要基于机制的预测药物不良反应，表现为由于合并摄入多种药物。这些方法需要集成和分析来自多个异构源的具有不同模式、实体符号和格式的生物医学数据和知识。为了应对这些综合性挑战，语义Web社区已经使用既定的W3C标准在生命科学关联开放数据（LSLOD）云中发布并链接了几个数据集。本文介绍了PhLeGrA药理学链接图分析平台。通过查询联邦，我们从LSLOD云中集成了四个源，并提取了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场（HCRF），这是一种用于结构化输出预测的判别潜变量模型。我们使用美国食品和药物管理局不良事件报告系统的数据集计算药物反应HCRF中的潜在概率分布。我们预测了146例因多种药物摄入而发生的不良反应，AUROC统计量大于0.75。PhLeGrA平台可以扩展到使用语义网技术发布的其他来源，以及发现其他类型的药理学关联。
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.