Abstract
Feature selection becomes a central task when ’signature’ profiles specific to a pathological status have to be extracted from high dimensional gene expression or proteomic data. In the present paper, we propose a feature selection method based on Singular Value Decomposition (SVD) and apply it to SELDI-TOF/MS proteomic data from a cohort of Type 2 Diabetics affected by Glomerulosclerosis and Membranous Nephropathy. We have selected a profile composed of 24 proteins that seems to be an effective signature for the pathology at hand, allowing to efficiently discriminate between the considered subtype of diabetes.
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References
Golub, T.R., Slonim, D.K., et al.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)
Ancona, N., Maglietta, R., D’Addabbo, A., et al.: Regularized Least Squares Cancer Classifiers from DNA microarray data. BMC-Bioinformatics 6 (Suppl 4):S2 (2005)
Ancona, N., Maglietta, R., Piepoli, A.: D’Addabbo, et al: On the statistical assessment of classifiers using DNA microarray data. BMC-Bioinformatics 7, 387 (2006)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1), 389–422 (2002)
Furlanello, C., Serafini, M., et al.: Entropy-based gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinf. 4, 54–73 (2003)
Yasui, Y., et al.: A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics 4(3), 449–463 (2003)
West, M., Blanchette, C.: Dressman, et al: Predicting the clinical status of human breast cancer by using gene expression profiles. PNAS 98(20), 11462–11467 (2001)
Mazzucco, G., et al.: Am. J. Kidney Dis., vol. 39, p. 713 (2002)
Vorderwulbecke, S., Cleverley, S., et al.: Protein quantification by the SELDI-TOF-MS based ProteinChip System. Nature Methods 2, 393–395 (2005)
Pisitkun, T., Shen, R.F., Knepper, A.: PNAS, vol. 101 (36), pp. 13368–13373 (2004)
Pisitkun, T., et al.: Molecular and Cellular Proteomics, vol. 5(10), pp. 1760–1771 (2006)
Rindler, M.J., et al.: J. Biol. Chem., vol. 265(34), pp. 20784–20789 (1990)
Fels, L.M., Bundschuh, I., Gwinner, W., et al.: Kidney Int. Suppl., vol. 47, pp. S81–S88 (1994)
Usuda, K., Kono, K., Dote, T., et al.: Arch Toxicol, vol. 72, pp. 104–109 (1998)
Nortier, J.L., Deschodt-Lanckman, M.M., et al.: Kidney Int., vol. 51, pp. 288–293 (1997)
Jungers, P., Hannedouche, T., et al.: Nephrol Dial Transplant, vol. 10, pp. 1353–1360 (1995)
Donaldio, C., Tramonti, G., Lucchesi, A., et al.: Ren Fail, vol. 20, pp. 319–324 (1998)
Lynn, K.L., Marshall, R.D.: Clin Nephrol, vol. 22, pp. 253–257 (1984)
Ambroise, C., McLachlan, G.J.: Selection bias in gene extraction on the basis of microarray gene-expression data. PNAS 99, 6562–6566 (2002)
Golub, G.H., Van Loan, C.F.: Matrix Computation. Johns Hopkins Univ. Press, Baltimore (1996)
Guyon, I., Elisseeff, A.: An introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Tikhonov, A.N., Arsenin, V.Y.: Solutions of ill-posed problems. W. H. Winston, Washington DC (1977)
Poggio, T., Girosi, F.: A Theory of Networks for Approximation and Learning. A. I. Laboratory, MIT, Cambridge (1989) A.I. Memo No. 1140
Girosi, F.: An Equivalence Between Sparse Approximation And Support Vector Machines. Neural Comp. 10(6), 1455–1480 (1998)
Mukherjee, S., Tamayo, P., Rogers, S., et al.: Estimating dataset size requirements for classifying dna microarray data. J. Comp. Biol. 10, 119–142 (2003)
Good, P.: Permutation tests: a practical guide to resampling methods for testing hypothesis. Springer, Heidelberg (1994)
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D’Addabbo, A. et al. (2008). SVD Based Feature Selection and Sample Classification of Proteomic Data. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85567-5_69
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DOI: https://doi.org/10.1007/978-3-540-85567-5_69
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