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Rank sum method for related gene selection and its application to tumor diagnosis

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Chinese Science Bulletin

Abstract

Tumor diagnosis by analyzing gene expression profiles becomes an interesting topic in bioinformatics and the main problem is to identify the genes related to a tumor. This paper proposes a rank sum method to identify the related genes based on the rank sum test theory in statistics. The tumor diagnosis system is constructed by the support vector machine (SVM) trained on the set of the related gene expression profiles. The experiments demonstrate that the constructed tumor diagnosis system with the rank sum method and SVM can reach an accuracy level of 96.2% on the colon data and 100% on the leukemia data.

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References

  1. Golub, T. R., Slonim, D. K., Tamayo, P. et al., Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science, 1999, 286: 531–537.

    Article  Google Scholar 

  2. Alon, U., Barkai, N., Notterman, D. A. et al., Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proc. Nat’l Acad. Sci.USA, 1999, 96: 6745–6750.

    Article  Google Scholar 

  3. Brown, M. P. S., Grundy, W. N., Lin D. et al., Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Nat’l Acad. Sci., 2000, 97(1): 262–267.

    Article  Google Scholar 

  4. Dudoit, S., Fridyand, J., Speed T. P., Comparison of discrimination methods for the classification of tumor using gene expression data, Journal of American Statistical Association, 2002, 97(457): 77–87.

    Article  Google Scholar 

  5. Furey, T., Cristianini, N., Duffy, N. et al., Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 2000, 16(10): 909–914.

    Article  Google Scholar 

  6. Guyon, I., Weston, J., Barnhill, S.et al., Gene selection for cancer classification using support vector machine, Machine Learning, 2002, 46(1/3): 389–422.

    Article  Google Scholar 

  7. Pavlidis, P., Weston, J., Cai, J.et al., Gene functional analysis from heterogeneous data, Proc. RECOMB, New York: ACM Press, 2001, 249–255.

    Google Scholar 

  8. Ding, H. Q., Analysis of gene expression profiles: class discovery and leaf ordering, Proc. RECOMB, New York: ACM Press, 2002, 127–136.

    Google Scholar 

  9. Goulden, C. H., Methods of Statistical Analysis, 2nd ed., New York: John Wiley & Sons, 1956.

    Google Scholar 

  10. Hettmansperger, T. P., Statistical Inference Based on Ranks, New York: John Wiley & Sons, Inc., 1984.

    Google Scholar 

  11. Nikitin, Y., Asymptotic Efficiency of Non-parametric Tests, New York: Cambridge University Press, 1995.

    Book  Google Scholar 

  12. Vapnik, V., The Nature of Statistical Learning Theory, New York: Sponger, 2000.

    Google Scholar 

  13. Joachims, T., Making large-scale SVM learning practical, Advances in Kernel Methods-Support Vector Learning (eds. Scholkopf, B., Burges, C, Smola, A. J. et al.), MIT-Press, 1999.

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Correspondence to Jinwen Ma.

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Deng, L., Ma, J. & Pei, J. Rank sum method for related gene selection and its application to tumor diagnosis. Chin.Sci.Bull. 49, 1652–1657 (2004). https://doi.org/10.1007/BF03184138

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  • DOI: https://doi.org/10.1007/BF03184138

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