Information storage and retrieval
Information storage and retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A vector space model for automatic indexing
Communications of the ACM
Modern Information Retrieval
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Probabilistic question answering on the Web: Research Articles
Journal of the American Society for Information Science and Technology
An Ontology-Driven Clustering Method for Supporting Gene Expression Analysis
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
A knowledge-driven approach to cluster validity assessment
Bioinformatics
Semantic similarity over the gene ontology: family correlation and selecting disjunctive ancestors
Proceedings of the 14th ACM international conference on Information and knowledge management
Correlation between Gene Expression and GO Semantic Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Vector Space Search Engine forWeb Services
ECOWS '05 Proceedings of the Third European Conference on Web Services
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Measuring semantic similarity between Gene Ontology terms
Data & Knowledge Engineering
Scoring and summarising gene product clusters using the Gene Ontology
International Journal of Data Mining and Bioinformatics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Advances in biological experiments, such as DNA microarrays, have produced large multidimensional data sets for examination and retrospective analysis. Scientists however, heavily rely on existing biomedical knowledge in order to fully analyze and comprehend such datasets. Our proposed framework relies on the Gene Ontology for integrating a priori biomedical knowledge into traditional data analysis approaches. We explore the impact of considering each aspect of the Gene Ontology individually for quantifying the biological relatedness between gene products. We discuss two figure of merit scores for quantifying the pair-wise biological relatedness between gene products and the intra-cluster biological coherency of groups of gene products. Finally, we perform cluster deterioration simulation experiments on a well scrutinized Saccharomyces cerevisiae data set consisting of hybridization measurements. The results presented illustrate a strong correlation between the devised cluster coherency figure of merit and the randomization of cluster membership.