Classification of proteomic data with multiclass Logistic Partial Least Squares algorithm.
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.
Liu
Zhenqiu
Z
Division of Biostatistics, Greenebaum Cancer Center, University of Maryland Medicine, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu
Chen
Dechang
D
Tian
Jianjun Paul
JP
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D016015:Logistic Models; D008297:Male; D013058:Mass Spectrometry / Q000379:methods*; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D020543:Proteome / Q000032:analysis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D020543:Proteome