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

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 (US)
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

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics
Clinical Biochemistry
Health Information Management

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1504/IJBRA.2008.017160

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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.",

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

year = "2008",

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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.

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Classification of proteomic data with multiclass Logistic Partial Least Squares algorithm

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

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