An information theoretic approach to assessing Gene-Ontology-driven similarity and its application

被引：0

作者：

Wang, Haiying ^{[1
]}

Azuaje, Francisco ^{[2
]}

Zheng, Huiru ^{[1
]}

机构：

[1] Univ Ulster, Sch Comp & Math, Comp Sci Res Inst, Belfast, Antrim, North Ireland

[2] Publ Res Ctr Hlth CRP Sante, Cardiovasc Res Lab, L-1150 Luxembourg, Luxembourg

来源：

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | 2014年 / 9卷 / 02期

关键词：

semantic similarity; GO; gene ontology; information content;

D O I：

10.1504/IJDMB.2014.059061

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Using information-theoretic approaches, this paper presents a cross-platform system to support the integration of Gene Ontology (GO)-driven similarity knowledge into functional genomics. Three GO-driven similarity measures (Resnik's, Lin's and Jiang's metrics) have been implemented to measure between-term similarity within each of the GO hierarchies. Two approaches (simple and highest average similarity) which are based on the aggregation of between-term similarities, are used to estimate the similarity between gene products. The system has been successfully applied to a number of applications including assessing gene expression correlation patterns and the relationships between GO-driven similarity and other functional properties.

引用

页码：121 / 134

页数：14

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