Microarray Time-Series Data Clustering Using Rough-Fuzzy C-Means Algorithm

Clustering is one of the important analysis in functional genomics that discovers groups of co-expressed genes from microarray data. In this paper, the application of rough-fuzzy c-means (RFCM) algorithm is presented to discover co-expressed gene clusters. One of the major issues of the RFCM based m...

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Vydáno v:2011 IEEE International Conference on Bioinformatics and Biomedicine s. 269 - 272
Hlavní autoři: Maji, Pradipta, Paul, Sushmita
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2011
Témata:
Algorithm design and analysis
Approximation methods
Clustering
Clustering algorithms
Fuzzy Sets
Gene expression
Indexes
Microarray
Ontologies
Partitioning algorithms
Rough Sets
ISBN:1457717999, 9781457717994
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Shrnutí:Clustering is one of the important analysis in functional genomics that discovers groups of co-expressed genes from microarray data. In this paper, the application of rough-fuzzy c-means (RFCM) algorithm is presented to discover co-expressed gene clusters. One of the major issues of the RFCM based microarray data clustering is how to select initial prototypes of different clusters. To overcome this limitation, a method is proposed to select initial cluster centers. It enables the RFCM algorithm to converge to an optimum or near optimum solutions and helps to discover co-expressed gene clusters. A method is also introduced based on Dunn's cluster validity index to identify optimum values of different parameters of the initialization method and the RFCM algorithm. The effectiveness of the RFCM algorithm, along with a comparison with other related methods, is demonstrated on five yeast gene expression time-series data sets using Silhouette index, Davies-Bouldin index, and gene ontology based analysis.
ISBN:1457717999
9781457717994
DOI:10.1109/BIBM.2011.14