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J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson and P. S. Meltzer, “Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks,” Nature Medicine, Vol. 7, No. 6, 2001, pp. 673-679. doi:10.1038/89044

has been cited by the following article:

• TITLE: Metasample-Based Robust Sparse Representation for Tumor Classification

  AUTHORS: Bin Gan, Chun-Hou Zheng, Jin-Xing Liu

  KEYWORDS: DNA Microarray Data; Sparse Representation Classification; MRSRC; Robust

  JOURNAL NAME: Engineering, Vol.5 No.5B, July 26, 2013

  ABSTRACT: In this paper, based on sparse representation classification and robust thought, we propose a new classifier, named MRSRC (Metasample Based Robust Sparse Representation Classificatier), for DNA microarray data classification. Firstly, we extract Metasample from trainning sample. Secondly, a weighted matrix W is added to solve an l1-regular- ized least square problem. Finally, the testing sample is classified according to the sparsity coefficient vector of it. The experimental results on the DNA microarray data classification prove that the proposed algorithm is efficient.