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Issue No.03 - May-June (2012 vol.9)
pp: 788-798
M. Krejnik , Dept. of Cybern., Czech Tech. Univ. in Prague, Prague, Czech Republic
The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed.
learning (artificial intelligence), bioinformatics, data analysis, feature extraction, genetics, biological sample, empirical evidence, functional clustering, gene expression classification, biological knowledge, gene-gene interactions, gene expression data, functionally related gene, gene clusters, classifier learning, benchmark data sets, gene expression clustering, feature extraction technique, Clustering algorithms, Bioinformatics, Algorithm design and analysis, Gene expression, Feature extraction, Partitioning algorithms, classification., Biological prior knowledge, gene expression, gene set analysis, clustering, feature extraction
M. Krejnik, "Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 3, pp. 788-798, May-June 2012, doi:10.1109/TCBB.2012.23
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