Applied multivariate statistical analysis
Applied multivariate statistical analysis
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
DHC: A Density-Based Hierarchical Clustering Method for Time Series Gene Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
International Journal of Bioinformatics Research and Applications
Network module extraction with positive and negative co-regulation
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. This paper proposes a suitable dissimilarity measure for gene expression time series data sets. It also presents a graph-based clustering method for finding clusters in gene expression time series data using the new dissimilarity measure. A comparison with other similarity measures used for gene expression data is presented; the new dissimilarity measure is found effective. The clustering method is used in experiments that use real-life datasets and has been found to perform satisfactorily.