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

机译:通过探索术语下的本体并建模不确定性来改进GO语义相似性度量

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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.
机译:结果:为了表明我们的方法可以潜在地改善任何语义相似性度量,我们在六种不同的语义相似性度量上进行了测试:Resnik(1999),Lin(1998)和Jiang and Conrath(1997)三种常用的度量;以及最近提出的三种措施:Pesquita等人的simUI,simGIC。 (2008); Couto等人的GraSM。 (2007);以及Couto和Silva(2011)。我们将这些改进的方法应用于酿酒酵母的GO注释,并测试了它们与序列相似性,mRNA共表达和蛋白质-蛋白质相互作用数据之间的关系。我们的结果一致表明,使用向下随机游走会导致更可靠的相似性度量。

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