Class discovery in gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Knowledge guided analysis of microarray data
Journal of Biomedical Informatics
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
Techniques for clustering gene expression data
Computers in Biology and Medicine
International Journal of Data Mining and Bioinformatics
Gene Ontology Assisted Exploratory Microarray Clustering and Its Application to Cancer
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficiently mining time-delayed gene expression patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using Gene Ontology annotations in exploratory microarray clustering to understand cancer etiology
Pattern Recognition Letters
Constructing and mapping fuzzy thematic clusters to higher ranks in a taxonomy
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Mining time-delayed coherent patterns in time series gene expression data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A hybrid cluster-lift method for the analysis of research activities
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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The soundness of clustering in the analysis of gene expression profiles and gene function prediction is based on the hypothesis that genes with similar expression profiles may imply strong correlations with their functions in the biological activities. Gene Ontology (GO) has become a well accepted standard in organizing gene function categories. Different gene function categories in GO can have very sophisticated relationships, such as ýpart ofý and ýoverlappingý. Until now, no clustering algorithm can generate gene clusters within which the relationships can naturally reflect those of gene function categories in the GO hierarchy. The failure in resembling the relationships may reduce the confidence of clustering in gene function prediction. In this paper, we present a new clustering technique, Smart Hierarchical Tendency Preserving clustering (SHTP-clustering), based on a bicluster model, Tendency Preserving cluster (TP-Cluster). By directly incorporating Gene Ontology information into the clustering process, the SHTP-clustering algorithm yields a TP-cluster tree within which any subtree can be well mapped to a part of the GO hierarchy. Our experiments on yeast cell cycle data demonstrate that this method is efficient and effective in generating the biological relevant TP-Clusters.