Abstract
The approach to identify clusters of genes represented both by expression values and Gene Ontology annotations, where cluster membership should not be in conflict with any of the representations is presented in the paper. The method enables to identify the genes that are differently clustered in different representations, what can lead to further analysis and interesting conclusions. The approach is based on the fuzzy clustering algorithms and the notion of proximity as the aggregation operation at the higher level than similarity matrices is performed. The approach is verified on two datasets: a small synthetic and real-world gene dataset.
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