Improving GO semantic similarity measures by exploring the ontology beneath the terms and modelling uncertainty

被引:68
作者
Yang, Haixuan
Nepusz, Tamas
Paccanaro, Alberto [1 ]
机构
[1] Univ London, Dept Comp Sci, Egham TW20 0EX, Surrey, England
基金
英国生物技术与生命科学研究理事会;
关键词
GENE ONTOLOGY; INFORMATION; IDENTIFICATION; PROTEINS;
D O I
10.1093/bioinformatics/bts129
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Results: To show that our approach can potentially improve any semantic similarity measure, we test it on six different semantic similarity measures: three commonly used measures by Resnik (1999), Lin (1998), and Jiang and Conrath (1997); and three recently proposed measures: simUI, simGIC by Pesquita et al. (2008); GraSM by Couto et al. (2007); and Couto and Silva (2011). We applied these improved measures to the GO annotations of the yeast Saccharomyces cerevisiae, and tested how they correlate with sequence similarity, mRNA co-expression and protein-protein interaction data. Our results consistently show that the use of downward random walks leads to more reliable similarity measures.
引用
收藏
页码:1383 / 1389
页数:7
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