Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
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
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Pattern-based similarity search for microarray data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Classification of microarray data with factor mixture models
Bioinformatics
A new method to measure the semantic similarity of GO terms
Bioinformatics
Query-driven module discovery in microarray data
Bioinformatics
Bioinformatics
A semi-supervised approach to projected clustering with applications to microarray data
International Journal of Data Mining and Bioinformatics
Fuzzy C-means method with empirical mode decomposition for clustering microarray data
International Journal of Data Mining and Bioinformatics
New cluster ensemble approach to integrative biological data analysis
International Journal of Data Mining and Bioinformatics
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Biclustering is an important analysis method on gene expression data for finding a subset of genes sharing compatible expression patterns. Although some biclustering algorithms have been proposed, few provided a query-driven approach for biologists to search the biclusters, which contain a certain gene of interest. In this paper, we proposed a generalised fuzzy-based approach, namely Weighted Fuzzy-based Maximum Similarity Biclustering (WF-MSB), for extracting a query-driven bicluster based on the user-defined reference gene. A fuzzy-based similarity measurement and condition weighting approach are used to extract significant biclusters in expression levels. Both of the most similar bicluster and the most dissimilar bicluster to the reference gene are discovered by WF-MSB. The proposed WF-MSB method was evaluated in comparison with MSBE on a real yeast microarray data and synthetic data sets. The experimental results show that WF-MSB can effectively find the biclusters with significant GO-based functional meanings.