Chen Huajun, Ding Li, Wu Zhaohui, Yu Tong, Dhanapalan Lavanya, Chen Jake Y
School of Computer Science, Zhejiang University.
Brief Bioinform. 2009 Mar;10(2):177-92. doi: 10.1093/bib/bbp002.
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 herb-drug interactions analysis.
语义网技术通过形式本体使数据语义明确，从而实现万维网上异构数据的集成。在本文中，我们调研了利用语义网技术来表示、集成和分析各种生物医学网络中的知识的可行性及技术现状。我们引入了一个新的概念框架——语义图挖掘，以使研究人员在网络数据分析中能够将图挖掘与本体推理相结合。通过四个案例研究，我们展示了语义图挖掘如何应用于疾病致病基因分析、基因本体类别相互作用分析、药物疗效分析和草药 - 药物相互作用分析。
