DISCOVERING THE HIDDEN INFORMATION OF GENE ONTOLOGY:INSIGHT FROM COMPLEX NETWORK ANALYSIS
Recently,ontology is widely used in many disciplines as a semantic representation.Gene Ontology is a good illustration of the advantage for the ontology being used as a shared controlled vocabulary in practical application.However, the scale and complexity of such ontologies are rapidly increased, which makes the structures of ontologies are too complicated to understand and use.This paper investigated the hidden information such as the topological features and the potential important terms of large scale ontology insight from complex networks analysis.Through the empirical study, this paper shows that the Gene Ontology displays the same topological features as complex networks,such as small world and important terms through some famous complex network centralization methods.According to the relevant literatures of GO terms in MEDLINE, this paper evaluated which centralization method is more suitable for ontology important concepts identifying, the experimental results indicated that the Betweenness Centrality is the most appropriate method among all evaluated centralization measures.However,further research is necessary to get more reasonable importance ranking of the ontology.
Gene Ontology complez network network analysis important terms discovering centrality measure
JIN-TAO TANG TING WANG JI WANG
School of Computer,National University of Defense Technology,Changsha 410073,China National Laboratory for Parallel and Distributed Processing,Changsha 410073,China
国际会议
南京
英文
1-9
2009-08-29（万方平台首次上网日期，不代表论文的发表时间）