Self-organizing maps
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
Fuzzy c-means clustering with prior biological knowledge
Journal of Biomedical Informatics
Ontology-Driven Co-clustering of Gene Expression Data
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Computing with words with the ontological self-organizing map
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Finding top-k similar pairs of objects annotated with terms from an ontology
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Efficiently finding similar objects on ontologies using earth mover's distance
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Computers in Biology and Medicine
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We propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The method is applied to group yeast (Saccharomyces cerevisiae) genes according to both expression profiles and Gene Ontology (GO) annotations. The combination of multiple databases is supposed to provide a better biological definition and separation of gene clusters. We compare different levels of genome-wide co-clustering by weighting the involved sources of information differently. Clustering quality is determined by both general and SOM-specific validation measures. Co-clustering relies on a sufficient correlation between the different datasets. We investigate in various experiments how much GO information is contained in the applied gene expression dataset and vice versa. The second major contribution is a visualization technique that applies the cluster structure of SOMs for a better biological interpretation of gene (expression) clusterings. Our GO term maps reveal functional neighborhoods between clusters forming biologically meaningful functional SOM regions. To cope with the high variety and specificity of GO terms, gene and cluster annotations are mapped to a reduced vocabulary of more general GO terms. In particular, this advances the ability of SOMs to act as gene function predictors.