Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Microarray Time-Series Data Clustering Using Rough-Fuzzy C-Means Algorithm
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
Rough Set Based Generalized Fuzzy -Means Algorithm and Quantitative Indices
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
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Clustering technique is one of the useful tools to elucidate similar patterns across large number of transcripts and to identify likely co-regulated genes. It attempts to partition the genes into groups exhibiting similar patterns of variation in expression level. An application of rough-fuzzy c-means (RFCM) algorithm is presented in this paper to discover co-expressed gene clusters. Selection of initial prototypes of different clusters is one of the major issues of the RFCM based microarray data clustering. The pearson correlation based initialization method is used to address this limitation. It enables the RFCM algorithm to discover co-expressed gene clusters. The effectiveness of the RFCM algorithm and the initialization method, along with a comparison with other related methods, is demonstrated on five yeast gene expression data sets using standard cluster validity indices and gene ontology based analysis.