Several cancer classifiers combined with PLS-DR for base on gene expression profile
Автор: JianGeng Li, Hui Li
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 4 Vol. 3, 2011 года.
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It is known that Logistic Regression coupled with Partial Least Squares dimension reduction (PLSDR-LD) is capable of extracting a great deal of useful information for classification from gene expression profile and getting a rather high classification accuracy rate. In this study, we replace the logistic function of Logistic Regression with several functions which are similar to logistic function in appearance, and apply these functions to the analysis of microarray data sets from two cancer gene expression studies. We compare these newly introduced models with PLSDR-LD proposed in the literature. The most effective models with good prediction precision are lastly provided through analyzing the results of two experiments.
Logistic Regression, Partial Least Squares, gene expression profile, PLSDR-LD
Короткий адрес: https://sciup.org/15011628
IDR: 15011628
Список литературы Several cancer classifiers combined with PLS-DR for base on gene expression profile
- Alon, U. Barkai,N. Notterman,D.A. Gish,K. Ybar,S. Mack,D. and Levine,A.J. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA,1999;96,6745-6750.
- Boulesteix,A.-L. PLS dimension reduction for classification of microarray data. Statistical Applications in Genetics and Molecular Biology. 2004;3(1),33.
- Boulesteix A L and Strimmer K, Partial least squares: a versatile tool for the analysis of high-dimensional genomic data[J]. BRIFINGS IN BIOINFORMATICS. VOL 8.NO 1. 32~44, Jan, 2007.
- D ai,J.J. Lieu,L. and Rocke,D. Dimension reduction for classification with gene expression data. Statistical Applications in Genetics and Molecular Biology 2006;5(1), Article 6.
- Terrence S. Furey, Nello Cristianini , Nigel Duffy, David W. Bednarski , Michèl Schummer and David Haussler. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000; v16, n10, 906-914.
- J.G. Liao Khew-Voon Chin. Logistic regression for disease classification using microarray data: model selection in a large p and small n case.Bioinformatics 2007; v23, n15,1945-1951.
- Jian-Nan Zhang, A-Li luo, Yong-Heng Zhao, Automated estimation of stellar fundamental parameters from low resolution spectra: the PLS method. Research in Astonomy and Astrophysics.June 2009;V9,n6,712-724.
- Menezes,J.c. Felicio,C.C. Bras,L.P. Lopes,J.A. and Cabrita,L. Comparison of PLS algorithms in gasoline and gas oil parameter monitoring with MIR and NIR. Chemometrics and Intelligent Laboratory Systems. 28 July 2005;V78,n1-2,74-80.
- Nguyen,D.V. and Rocke,D.M., Tumor classification via partial least squares using microaray gene expession data. Bioinformatics, 2002(a),18(1),30-50.
- Nguyen,D.V. and Rocke,D.M, Multi-class cancer classification via partial least squares with gene expression profiles. Bioinformatics, 2002(b),18(9),1216-1226.
- Wold,H, Estimation of principal components and related models by iterative least squares. Krishnaiah PR (ed). Multivariate Analysis. New York: Academic Press, 1966; 391-420.
- W old,H, Nonlinear Iterative Partial Least Squares(NIPALS) modeling: some current development. Krishnaiah PR (ed). Multivariate Analysis. New York: Academic Press, 1973; 383-407.
- Wold,H, Path models with latent varibles: the NIPALS approach. Blalock HM(ed). Quantitative Sociology: Internatioanl Perpectives on Mathematical and Statistical Model Building. New York: Academic Press, 1975.
- Xue-Qiang ZengGuo-ZhengLi Geng-FengWu JackY.Yang M.Q. Irrelevant gene elimination for Partial Least Squares based Dimension Reduction by using feature probes. International Journal of Data Mining and Bioinformatics. 2009;v3,n1,85-103.
