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An extensive comparison of recent classification tools applied to microarray data

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  • Lee, Jae Won
  • Lee, Jung Bok
  • Park, Mira
  • Song, Seuck Heun

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  • Lee, Jae Won & Lee, Jung Bok & Park, Mira & Song, Seuck Heun, 2005. "An extensive comparison of recent classification tools applied to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 869-885, April.
    • Handle: RePEc:eee:csdana:v:48:y:2005:i:4:p:869-885
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    References listed on IDEAS

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    1. Gentleman R., 2003. "Resampling Methods: A Practical Guide to Data Analysis (2nd ed.)," The American Statistician, American Statistical Association, vol. 57, pages 70-70, February.
    2. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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    Cited by:

    1. Frénay, Benoît & Doquire, Gauthier & Verleysen, Michel, 2014. "Estimating mutual information for feature selection in the presence of label noise," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 832-848.
    2. Lambert-Lacroix, Sophie & Peyre, Julie, 2006. "Local likelihood regression in generalized linear single-index models with applications to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 2091-2113, December.
    3. Herbert Pang & Tiejun Tong & Hongyu Zhao, 2009. "Shrinkage-based Diagonal Discriminant Analysis and Its Applications in High-Dimensional Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1021-1029, December.
    4. Dennis Kostka & Rainer Spang, 2008. "Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures," PLOS Computational Biology, Public Library of Science, vol. 4(2), pages 1-6, February.
    5. Scrucca, Luca, 2007. "Class prediction and gene selection for DNA microarrays using regularized sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 438-451, September.
    6. Yang, Tae Young, 2009. "Simple Bayesian binary framework for discovering significant genes and classifying cancer diagnosis," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1743-1754, March.
    7. Yang, Tae Young, 2009. "Efficient multi-class cancer diagnosis algorithm, using a global similarity pattern," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 756-765, January.
    8. Jain Yashita & Ding Shanshan & Qiu Jing, 2019. "Sliced inverse regression for integrative multi-omics data analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(1), pages 1-13, February.
    9. Binbing Yu, 2009. "Approximating the risk score for disease diagnosis using MARS," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(7), pages 769-778.
    10. Shieh Albert D & Hung Yeung Sam, 2009. "Detecting Outlier Samples in Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-26, February.
    11. Pires, Ana M. & Branco, João A., 2010. "Projection-pursuit approach to robust linear discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2464-2485, November.
    12. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.
    13. Anne-Laure Boulesteix & Robert Hable & Sabine Lauer & Manuel J. A. Eugster, 2015. "A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies," The American Statistician, Taylor & Francis Journals, vol. 69(3), pages 201-212, August.
    14. Song Huang & Tiejun Tong & Hongyu Zhao, 2010. "Bias-Corrected Diagonal Discriminant Rules for High-Dimensional Classification," Biometrics, The International Biometric Society, vol. 66(4), pages 1096-1106, December.
    15. Valkenborg Dirk & Van Sanden Suzy & Lin Dan & Kasim Adetayo & Zhu Qi & Haldermans Philippe & Jansen Ivy & Shkedy Ziv & Burzykowski Tomasz, 2008. "A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-22, March.
    16. Jong Victor L. & Novianti Putri W. & Roes Kit C.B. & Eijkemans Marinus J.C., 2014. "Exploring homogeneity of correlation structures of gene expression datasets within and between etiological disease categories," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(6), pages 717-732, December.
    17. Junfei Chen & Qian Li & Huimin Wang & Menghua Deng, 2019. "A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China," IJERPH, MDPI, vol. 17(1), pages 1-21, December.
    18. Dong, Kai & Pang, Herbert & Tong, Tiejun & Genton, Marc G., 2016. "Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 127-142.
    19. Alan R Dabney & John D Storey, 2007. "Optimality Driven Nearest Centroid Classification from Genomic Data," PLOS ONE, Public Library of Science, vol. 2(10), pages 1-7, October.
    20. Conde David & Salvador Bonifacio & Rueda Cristina & Fernández Miguel A., 2013. "Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(5), pages 583-602, October.

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