Metrics for GO based protein semantic similarity: a systematic evaluation

By Catia Pesquita, Daniel Faria, Hugo P. Bastos, António E. N. Ferreira, André O. Falcão, Francisco M. Couto. BMC Bioinformatics, 2008

359 citations recorded by the source corpus.

BackgroundSeveral semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations.ResultsWe conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation.ConclusionsThis work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid simGIC was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ffcb7045b997e87c0f7bdbd12dd1ea865ebe1299

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Citations in the corpus

The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. (2008, background): “Additionally,anum-ber of computational algorithms have been developed for ontological analysis after the success of GO (e.g., 19,24,25 ) and can now be applied to human phenotype data.”
Quantifying Protein Function Specificity in the Gene Ontology (2010, background): “The probability of a term occurring t p is the frequency with which that particular term or any of p(t) ancestors occur in a data set [9,10].”
Semantic Similarity in Biomedical Ontologies (2009, methodology): “simGIC, slightly surpassing Resnik’s measure using the ‘‘pairwisebest pairs-average’’ approach in resolution [44].”