Semantic web for integrated network analysis in biomedicine
Semantic web for integrated network analysis in biomedicine
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DOI：
10.1093/bib/bbp002
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发表时间：
2009-03-01
影响因子：
9.5
通讯作者：
Chen, Jake Y.
中科院分区：
文献类型：
--
作者：
Chen, Huajun;Ding, Li;Chen, Jake Y.
语义Web技术通过形式化本体使数据的语义显式化，从而实现万维网上异构数据的集成。在这篇文章中，我们调查的可行性和利用语义Web技术来表示，集成和分析各种生物医学网络中的知识的艺术状态。我们引入了一个新的概念框架，语义图挖掘，使研究人员在网络数据分析中集成图挖掘与本体推理。通过四个案例研究，我们展示了如何语义图挖掘可以应用于分析的致病基因，基因本体类别的串扰，药物疗效分析和草药相互作用分析。
The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herbdrug interactions analysis.