Machine Learning
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A Rough Set Framework for Learning in a Directed Acyclic Graph
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Computational intelligence in bioinformatics
Transactions on Rough Sets III
Supervised learning in the gene ontology part i: a rough set framework
Transactions on Rough Sets IV
Supervised learning in the gene ontology part II: a bottom-up algorithm
Transactions on Rough Sets IV
A framework for reasoning with rough sets
Transactions on Rough Sets IV
Context-based knowledge discovery and its application
DM-IKM '12 Proceedings of the Data Mining and Intelligent Knowledge Management Workshop
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Prediction of gene function from expression profiles is an intriguing problem that has been attempted with both unsupervised clustering and supervised learning methods. By the incorporation of prior knowledge concerning gene function, supervised methods avoid some of the problems with clustering. However, even supervised methods ignore the fact that the functional classes associated with genes are typically organized in an ontology. Hence, we introduce a new supervised method for learning in such an ontology. It is tested on both an artificial data set and a data set containing measurements from human fibroblast cells. We also give an approach for measuring the classification performance in an ontology.