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Home > Archive>Volume 21, Issue 11, 2010 >2838-2851
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Classification Algorithm Combined GCM with CCM in Cancer Recognition
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    Abstract:

    In this paper, two cancer recognition models, global component model (GCM) and cancer component model (CCM), are constructed. Due to the fact that GCM and CCM complement each other, a weighted voting strategy is applied, and an ensemble algorithm based on GCM and CCM for cancer recognition (EAGC) is proposed. Independent test experiments and cross validation experiments are conducted on Leukemia, Breast, Prostate, DLBCL, Colon, and Ovarian cancer dataset, respectively, and EAGC performed well on all datasets. The experimental results show that recognition, solution, and the generalization are strengthened by the combination of GCM and CCM.

    Reference
    [1] Kuramochi M, Karypis G. Gene classification using expression profiles: A feasibility study. Int’l Journal on Artificial Intelligence Tools, 2005,14(4):641?660.
    [2] Li X, Zhang TW, Guo Z. An novel ensemble method of feature gene selection based on recursive partition-tree. Chinese Journal of Computers, 2004,27(5):675?682 (in Chinese with English abstract).
    [3] Lu XG, Lin YP, Wang HJ, Zhou SW, Li XL. A novel relative space based gene feature extraction and cancer recognition. In: Zhou ZH, Li H, Yang Q, eds. Proc. of the 11th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD 2007). Berlin: Springer-Verlag, 2007. 712?719.
    [4] Li YX, Li JG, Ruan XG. Study of informative gene selection for tissue classification based on tumor gene expression profiles. Chinese Journal of Computers, 2006,26(2):324?330 (in Chinese with English abstract).
    [5] Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RCT Gaasenbeek M, Angelo M, Reich M, Pinkus GS, Ray TS, Koval MA, Last KW, Norton A, Lister TA, Mesirov J, Neuberg DS, Lander ES, Aster JC, Golub TR. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine, 2002,8(1):68?74. [doi: 10.1038/nm0102-68]
    [6] Golub TR, Slonim DK, Tamayo P, Huard C, Gaasernbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 1999,286(5439):531?537. [doi: 10.1126/science.286.5439.531]
    [7] Veer LJVT, Dai H, Vijver MJVD, He YD, Hart AA, Mao M, Peterse HL, Kooy KVD, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002,415(6871):530?536. [doi: 10.1038/415530a]
    [8] Cho SB, Won HH. Machine learning in DNA microarray analysis for cancer classification. In: Chen YPP, ed. Proc. of the 1st Asia- Pacific Bioinformatics Conf. (APBC 2003). Adelaide: Australian Computer Society, 2003. 189?198.
    [9] Wang ZQ, Chenh SF, Chen ZQ. An optimized neural network linear ensemble for classification. Journal of Software, 2005,16(11): 1902?1908 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/16/1902.htm [doi: 10.1360/jos161902]
    [10] Tan AC, Gilbert D. Ensemble machine learning on gene expression data for cancer classification. Applied Bioinfomatics, 2003, 2(Suppl.):S75?S83.
    [11] Khan J, Wei JS, Ringne M, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 2001,7(6):673?679. [doi: 10.1038/89044]
    [12] O’Neill MC, Song L. Neural network analysis of lymphoma microarray data: Prognosis and diagnosis near-perfect. BMC Bioinformatics, 2003,4:13. [doi: 10.1186/1471-2105-4-13]
    [13] Liu B, Cui QH, Jiang TZ, Ma SD. A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinformatics, 2004,5:136. [doi: 10.1186/1471-2105-5-136]
    [14] Fayyad U, Irani K. Multi-Interval discretization of continuous-valued attributes for classification learning. In: Bajcsy R, ed. Proc. of the 13th Int’l Joint Conf. on Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers, 1993. 1022?1029.
    附中文参考文献: [2] 李霞,张田文,郭政.一种基于递归分类树的集成特征基因选择方法.计算机学报,2004,27(5):675?682.
    [4] 李颖新,李建更,阮晓钢.肿瘤基因表达谱分类特征基因选取问题及分析方法研究.计算机学报,2006,26(2):324?330.
    [9] 王正群,陈世福,陈兆乾.优化分类型神经网络线性集成.软件学报,2005,16(11):1902?1908. http://www.jos.org.cn/1000-9825/16/ 1902.htm [doi: 10.1360/jos161902]
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卢新国,林亚平,骆嘉伟,李丹.癌症识别中一种基于组合GCM和CCM的分类算法.软件学报,2010,21(11):2838-2851

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History
  • Received:October 14,2008
  • Revised:July 07,2009
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