Classification of proteomic data with multiclass Logistic Partial Least Squares algorithm

Authors:
Zhenqiu Liu;Dechang Chen;Jianjun Paul Tian
Affiliations:
Division of Biostatistics, Greenebaum Cancer Center, University of Maryland Medicine, 22 South Greene Street, Baltimore, 21201 MD, USA.;Department of Preventive Medicine and Biometrics, Uniformed Services, University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, 20814 MD, USA.;Mathematics Department, College of William and Mary, Williamsburg, 23187, VA, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2008

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Abstract

Early detection of cancer is crucial for successful treatments. In this paper, we propose a multiclass Logistic Partial Least Squares (LPLS) algorithm for classification of normal vs. cancer using Mass Spectrometry (MS). LPLS combines the multiclass logistic regression with Partial Least Squares (PLS) algorithm. Wavelet decomposition is also proposed for pre-processing of original data. Wavelet decomposition and the proposed LPLS are applied to real life cancer data. Experimental comparisons show that LPLS with wavelet decomposition outperforms other methods in the analysis of MS data.