CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2008 vol.5 Issue No.01  JanuaryMarch
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Issue No.01  JanuaryMarch (2008 vol.5)
pp: 120135
ABSTRACT
When analyzing the results of microarray experiments, biologists generally use unsupervised categorization tools. However, such tools regard each time point as an independent dimension and utilize the Euclidean distance to compute the similarities between expressions. Furthermore, some of these methods require the number of clusters to be determined in advance, which is clearly impossible in the case of a new dataset. Therefore, this study proposes a novel scheme, designated as the Variationbased Coexpression Detection (VCD) algorithm, to analyze the trends of expressions based on their variation over time. The proposed algorithm has two advantages. First, it is unnecessary to determine the number of clusters in advance since the algorithm automatically detects those genes whose profiles are grouped together and creates patterns for these groups. Second, the algorithm features a new measurement criterion for calculating the degree of change of the expressions between adjacent time points and evaluating their trend similarities. Three realworld microarray datasets are employed to evaluate the performance of the proposed algorithm.
INDEX TERMS
Pattern analysis, Time series analysis, Bioinformatics, Data mining, Clustering, Gene expression
CITATION
ZongXian Yin, JungHsien Chiang, "Novel Algorithm for Coexpression Detection in TimeVarying Microarray Data Sets", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.5, no. 1, pp. 120135, JanuaryMarch 2008, doi:10.1109/tcbb.2007.1052
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