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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107030

    Title: Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm
    Authors: Chen, K.-H.;Wang, K.-J.;Tsai, M.-L.;Wang, K.-M.;Adrian, A-M.;Cheng, W.-C.;Yang, T.-S.;Teng, N.-C.;Tan, K.-P.;Chang, K.-S.
    Keywords: Gene expression;Cancer;Particle swarm optimization;Decision tree classifier
    Date: 2014-02-07
    Issue Date: 2016-08-15
    Publisher: BioMed Central Ltd.
    Abstract: Background
    In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.

    To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.

    Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.
    Relation: BMC Bioinformatics 15(49)
    DOI: 10.1186/1471-2105-15-49
    Appears in Collections:[企業管理學系暨研究所] 期刊論文

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