标题： 基于化学生物学数据的语义推理用于药物发现 显示英文标题

标题： Semantic Inference using Chemogenomics Data for Drug Discovery

摘要： 背景 语义网络技术（SWT）使得整合和搜索公共领域中大量生命科学数据集成为可能，这已通过LODD、Bio2RDF和Chem2Bio2RDF等知名链接数据项目得到证明。 这些数据集的整合创建了大型的信息网络。 我们之前描述了一种名为WENDI的工具，用于汇总与新化学化合物相关的信息，有效地创建了将化合物与基因、疾病等联系起来的证据路径。 在本文中，我们研究了基于这些证据路径的语义标记版本、规则集和推理引擎自动推断新的化合物-疾病关联（从而推断网络中的新链接）的实用性。 结果 通过实现语义推理算法、规则集、语义网络方法（RDF、OWL和SPARQL）和新界面，我们创建了一个名为Chemogenomic Explorer的新工具，该工具使用本体论注释的RDF语句网络以及演绎推理工具，从WENDI结果中推断查询结构与基因和疾病之间的新关联。 然后该工具允许对这些证据路径进行交互式聚类和过滤。 结论 我们提出了一种新的综合方法，用于利用语义推理推断化学化合物和疾病之间的链接。 这种方法允许使用规则集和语义注释的数据识别化合物和疾病之间的多条证据路径，并将这些证据路径聚类以显示化合物与疾病之间的整体证据。 我们认为这是一种强大的方法，因为它允许根据支持它们的证据量对化合物-疾病关系进行排序。 显示英文摘要

摘要： Background Semantic Web Technology (SWT) makes it possible to integrate and search the large volume of life science datasets in the public domain, as demonstrated by well-known linked data projects such as LODD, Bio2RDF, and Chem2Bio2RDF. Integration of these sets creates large networks of information. We have previously described a tool called WENDI for aggregating information pertaining to new chemical compounds, effectively creating evidence paths relating the compounds to genes, diseases and so on. In this paper we examine the utility of automatically inferring new compound-disease associations (and thus new links in the network) based on semantically marked-up versions of these evidence paths, rule-sets and inference engines. Results Through the implementation of a semantic inference algorithm, rule set, Semantic Web methods (RDF, OWL and SPARQL) and new interfaces, we have created a new tool called Chemogenomic Explorer that uses networks of ontologically annotated RDF statements along with deductive reasoning tools to infer new associations between the query structure and genes and diseases from WENDI results. The tool then permits interactive clustering and filtering of these evidence paths. Conclusions We present a new aggregate approach to inferring links between chemical compounds and diseases using semantic inference. This approach allows multiple evidence paths between compounds and diseases to be identified using a rule-set and semantically annotated data, and for these evidence paths to be clustered to show overall evidence linking the compound to a disease. We believe this is a powerful approach, because it allows compound-disease relationships to be ranked by the amount of evidence supporting them.