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Clustering Gene Expression Data by MCL Algorithm  

Shon, Ho-Sun (School of Electrical and Computer Engineering, Chungbuk National University)
Ryu, Keun-Ho (School of Electrical and Computer Engineering, Chungbuk National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.45, no.4, 2008 , pp. 27-33 More about this Journal
Abstract
The clustering of gene expression data is used to analyze the results of microarray studies. This clustering is one of the frequently used methods in understanding degrees of biological change and gene expression. In biological research, MCL algorithm is an algorithm that clusters nodes within a graph, and is quick and efficient. We have modified the existing MCL algorithm and applied it to microarray data. In applying the MCL algorithm we put forth a simulation that adjusts two factors, namely inflation and diagonal tent and converted them by making use of Markov matrix. Furthermore, in order to distinguish class more clearly in the modified MCL algorithm we took the average of each row and used it as a threshold. Therefore, the improved algorithm can increase accuracy better than the existing ones. In other words, in the actual experiment, it showed an average of 70% accuracy when compared with an existing class. We also compared the MCL algorithm with the self-organizing map(SOM) clustering, K-means clustering and hierarchical clustering (HC) algorithms. And the result showed that it showed better results than ones derived from hierarchical clustering and K-means method.
Keywords
Gene Expression; MCL algorithm; Clustering; Hierarchical; K-means;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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