A new approach to analyzing gene expression time series data
Proceedings of the sixth 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
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Possibilistic approach for biclustering microarray data
Computers in Biology and Medicine
Novel Algorithm for Coexpression Detection in Time-Varying Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Expert Systems with Applications: An International Journal
A fuzzy biclustering algorithm for social annotations
Journal of Information Science
Methods to bicluster validation and comparison in microarray data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A network flow model for biclustering via optimal re-ordering of data matrices
Journal of Global Optimization
BSN: An automatic generation algorithm of social network data
Journal of Systems and Software
BARTMAP: A viable structure for biclustering
Neural Networks
A new measure for gene expression biclustering based on non-parametric correlation
Computer Methods and Programs in Biomedicine
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Microarrays have become a standard tool for investigating gene function and more complex microarray experiments are increasingly being conducted. For example, an experiment may involve samples from several groups or may investigate changes in gene expression over time for several subjects, leading to large three-way data sets. In response to this increase in data complexity, we propose some extensions to the plaid model, a biclustering method developed for the analysis of gene expression data. This model-based method lends itself to the incorporation of any additional structure such as external grouping or repeated measures. We describe how the extended models may be fitted and illustrate their use on real data.