An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Validating associations in biological databases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Measuring semantic similarity between Gene Ontology terms
Data & Knowledge Engineering
Journal of Biomedical Informatics
Snippet Generation for Semantic Web Search Engines
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
A go-driven semantic similarity measure for quantifying the biological relatedness of gene products
Intelligent Decision Technologies - Special issue on advances in medical intelligent decision support systems
A method for similarity-based grouping of biological data
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Evaluation of gene ontology semantic similarities on protein interaction datasets
International Journal of Bioinformatics Research and Applications
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Many bioinformatics applications would benefit from comparing proteins based on their biological role rather than their sequence. In most biological databases, proteins are already annotated with ontology terms. Previous studies identified a correlation between the sequence similarity and the semantic similarity of proteins. The semantic similarity of proteins was computed from their annotated GO terms. However, proteins sharing a biological role do not necessarily have a similar sequence.This paper introduces our study of the correlation between GO and family similarity. Family similarity overcomes some of the limitations of sequence similarity, thus we obtained a strong correlation between GO and family similarity. Additionally, this paper introduces GraSM, a novel method that uses all the information in the graph structure of the GO, instead of considering it as a hierarchical tree. When calculating the semantic similarity of two concepts, GraSM selects the disjunctive common ancestors rather than only using the most informative common ancestor. GraSM produced a higher family similarity correlation than the original semantic similarity measures.