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Neighbor Method to the Classification of Renal Cell Carcinoma
Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE
  • Dongqing Liu, University of Akron
  • Ting Shi, Cleveland Clinic Foundation
  • John A. DiDonato, Cleveland Clinic Foundation
  • Joseph D. Carpten, Translational Genomics Research Institute
  • Jianping Zhu, Cleveland State University
  • Zhong-Hui Duan, University of Akron
Document Type
Conference Proceeding
Publication Date
In this study, we use a genetic algorithm and k-nearest neighbor method to classify two subtypes of renal cell carcinoma using a set of microarray gene expression profiles of nine samples (three clear cell tumors and six papillary tumors). We show that the genetic algorithm/k-nearest neighbor method can be efficiently used in identifying a panel of discriminator genes. To test the robustness of the algorithm, we perform a bootstrapping analysis that removes one sample from the data set at a time and uses the remaining samples for gene selection. We show that each of the removed samples can be classified correctly. We also analyze the stability of the algorithm and the sensitivity of the algorithm with respect to different samples.
Citation Information
Liu, D., Shi, T., DiDonato, J., Carpten, J., Zhu, J., and Duan, Z. (2004), Application of genetic algorithm/K-nearest neighbor method to the classification of renal cell carcinoma, in Proceedings of Computational Systems Bioinformatics Conference, 558 – 559, IEEE Computer Society Press, Los Alamitos, CA.