A Topology-Based Metric for Measuring Term Similarity in the Gene Ontology

Table 4

Comparison of performance of our approach with Wang et al., Zhang et al. and annotation-based ones using Pearson’s correlation with enzyme Commission (eC), Pfam and sequence similarity, and resolution. Results are obtained from the CESSM online tool. For each ontology, the top two best scores among 12 approaches are in bold.

Ontology	Approaches		Similarity measure correlation			Resolution
			EC	PFAM	Seq Sim
BP	GO-Universal	(BMA)	0.44287	0.53919	0.76797	0.90067
	Wang et al.		0.43266	0.46692	0.63356	0.90966
	Zhang et al.		0.21944	0.26495	0.20270	0.30148
	Resnik	Avg	0.30218	0.32324	0.40685	0.33673
		Max	0.30756	0.26268	0.30273	0.64522
		BMA	0.44441	0.45878	0.73973	0.90041
	Term-based	SimUIC	0.38458	0.43693	0.74410	0.84503
		SimGIC	0.39811	0.45470	0.77326	0.83730
MF	GO-Universal	(BMA)	0.73886	0.60285	0.55163	0.52905
	Wang et al.		0.65910	0.49101	0.37101	0.33109
	Zhang et al.		0.49753	0.41147	0.32235	0.39865
	Resnik	Avg	0.39635	0.44038	0.50143	0.41490
		Max	0.45393	0.18152	0.12458	0.38056
		BMA	0.60271	0.57183	0.66832	0.95771
	Term-based	SimUIC	0.65826	0.62510	0.60512	0.96928
		SimGIC	0.62196	0.63806	0.71716	0.95590