Clustering pair-wise dissimilarity data into partially ordered sets
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
An information theoretic approach to assessing gene-ontology-driven similarity and its application
International Journal of Data Mining and Bioinformatics
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into clustering-based gene expression analysis. A system that integrates GO annotations, similarity patterns and expression data in yeast is assessed. In comparison with a clustering model based only on expression data correlation, the proposed framework not only produces consistent results, but also it offers alternative, potentially meaningful views of the biological problem under study. Moreover, it provides the basis for developing other automated, knowledge-driven data mining systems in this and related application areas.