Semantic inference using chemogenomics data for drug discovery
Semantic inference using chemogenomics data for drug discovery
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DOI：
10.1186/1471-2105-12-256
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发表时间：
2011-06-23
影响因子：
3
通讯作者：
Wild, David J.
中科院分区：
文献类型：
--
作者：
Zhu, Qian;Sun, Yuyin;Wild, David J.
背景资料：语义网技术（Semantic Web Technology，SWT）使得公共领域中大量生命科学数据集的集成和检索成为可能，如著名的链接数据项目LODD、Bio 2 RDF和Chem 2Bio 2 RDF。这些集合的整合创造了巨大的信息网络。我们之前描述了一个名为WENDI的工具，用于聚合与新化合物有关的信息，有效地创建将化合物与基因、疾病等关联起来的证据路径。基于这些证据路径、规则集和推理引擎的语义标记版本，搜索网络中的新链接。结果：通过实施的语义推理算法，规则集，语义Web方法（RDF，OWL和SPARQL）和新的接口，我们已经创建了一个新的工具，称为Chemogenic Explorer，它使用网络的本体注释RDF语句沿着与演绎推理工具推断新的关联查询结构和基因和疾病的WENDI结果。然后，该工具允许互动聚类和过滤这些证据paths.Conclusions：我们提出了一个新的聚合方法来推断化学化合物和疾病之间的联系，使用语义推理。这种方法允许使用规则集和语义注释数据来识别化合物和疾病之间的多个证据路径，并且对这些证据路径进行聚类以显示将化合物与疾病联系起来的总体证据。我们相信这是一种强大的方法，因为它允许根据支持它们的证据数量对化合物-疾病关系进行排名。
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.