Classification of proteomic data with multiclass Logistic Partial Least Squares algorithm

Zhenqiu Liu; Dechang Chen; Jianjun Paul Tian

doi:10.1504/IJBRA.2008.017160

Classification of proteomic data with multiclass Logistic Partial Least Squares algorithm

Zhenqiu Liu^*, Dechang Chen, Jianjun Paul Tian

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English
Pages (from-to)	1-10
Number of pages	10
Journal	International Journal of Bioinformatics Research and Applications
Volume	4
Issue number	1
DOIs	https://doi.org/10.1504/IJBRA.2008.017160
State	Published - Feb 2008
Externally published	Yes

Keywords

Bioinformatics
Logistic regression
MS
Mass spectrometry
PLS
Partial least squares
Wavelet

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10.1504/IJBRA.2008.017160

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title = "Classification of proteomic data with multiclass Logistic Partial Least Squares algorithm",

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.",

keywords = "Bioinformatics, Logistic regression, MS, Mass spectrometry, PLS, Partial least squares, Wavelet",

author = "Zhenqiu Liu and Dechang Chen and Tian, {Jianjun Paul}",

year = "2008",

month = feb,

doi = "10.1504/IJBRA.2008.017160",

language = "English",

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journal = "International Journal of Bioinformatics Research and Applications",

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AU - Liu, Zhenqiu

AU - Chen, Dechang

AU - Tian, Jianjun Paul

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N2 - 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.

AB - 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.

KW - Bioinformatics

KW - Logistic regression

KW - MS

KW - Mass spectrometry

KW - PLS

KW - Partial least squares

KW - Wavelet

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JO - International Journal of Bioinformatics Research and Applications

JF - International Journal of Bioinformatics Research and Applications

IS - 1

ER -

Classification of proteomic data with multiclass Logistic Partial Least Squares algorithm

Abstract

Keywords

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