Metrics for GO based protein semantic similarity: a systematic evaluation.
BACKGROUND
Several 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.


RESULTS
We 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.


CONCLUSIONS
This 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.
Pesquita
Catia
C
XLDB, Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande-Edifício C6, Lisboa, Portugal. cpesquita@xldb.di.fc.ul.pt
Faria
Daniel
D
Bastos
Hugo
H
Ferreira
António E N
AE
Falcão
André O
AO
Couto
Francisco M
FM
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods* / Q000592:standards; D030562:Databases, Protein / Q000592:standards; D020869:Gene Expression Profiling / Q000145:classification / Q000592:standards; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000592:standards / Q000706:statistics & numerical data; D011506:Proteins / Q000145:classification* / Q000235:genetics / Q000648:ultrastructure; D012015:Reference Standards; D012016:Reference Values; D012660:Semantics; D012680:Sensitivity and Specificity; D017385:Sequence Homology; D013329:Structure-Activity Relationship; D018875:Vocabulary, Controlled
D011506:Proteins