Consensus Maximum Margin Criterion for Classification of Proteomic Profile

Xiao Li Yang; Dong Xia Li

doi:10.4028/www.scientific.net/AMM.556-562.351

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Consensus Maximum Margin Criterion for...

Consensus Maximum Margin Criterion for Classification of Proteomic Profile

Abstract:

We propose a new technique for cancer diagnosis from proteomic profile, which mainly contains four steps. Firstly, the original profiles are preprocessed by baseline correction, denoising, compression and rescaling. Then, the profiles are modelled by maximum margin criterion (MMC) analysis. After that, a consensus technique is utilized to identify the discriminant features. Finally, support vector machine (SVM) is applied to classify the modelling data. To study the validity of the proposed method, it is worked on classifying two proteomic profile datasets, the normal and the cancer samples. The results show that the method is efficient.

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Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

351-354

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.351

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Online since:

May 2014

Authors:

Xiao Li Yang*, Dong Xia Li

Keywords:

Classification, Maximum Margin Criterion, Proteomic Profile, Support Vector Machine (SVM)

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* - Corresponding Author

References

[1] V. Seibert, A. Wiesner, T. Bushmann and J. Meuer: Pathol. Res. Pract. Vol. 200(2004), p.83.

Google Scholar

[2] E.P. Diamandis: Clin. Chem. Vol. 49(2003), p.1272.

Google Scholar

[3] E.F. Petricoin and L.A. Liotta: Curr. Opin. Biotech. Vol. 15(2004), p.24.

Google Scholar

[4] K.P. ]Rosenblatt, P.B. Greenwood, J.K. Killian and A. Mehta: Annu. Rev. Med. Vol. 55(2004), p.97.

Google Scholar

[5] L. Lancashire, O. Schmid, H. Shah and G. Ball: Bioinform. Vol. 21(2005), p.2191.

Google Scholar

[6] O.P. Whelehan, M.E. Earll, E. Johansson and M. Toft: Chemometr. Intell. Lab. Sys. Vol. 84(2006), p.82.

Google Scholar

[7] Z.X. Wang, Y.H. Liu and L. Bai: Future BioMedical Information Engineering 2008, p.30.

Google Scholar

[8] D. Mantini, F. Petrucci, P.D. Boccio, D. Pieragostino, M.D. Nicola, A. Lugaresi, G. Federici, P. Sacchetta, C. Dillio and A. Urbani: Bioinform. Vol. 24(2007), p.63.

DOI: 10.1093/bioinformatics/btm533

Google Scholar

[9] H. Li, T. Jiang and K. Zhang: IEEE T. Neural Networ. Vol. 17(2006), p.157.

Google Scholar

[10] X. Yang, Q. He, S. Yang and L. Liu: The 5th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2011) Vol. 1, p.15.

Google Scholar

[11] X. Yang and Y. Yang: Comput. & Appli. Chem. Vol. 27(2010), p.1498.

Google Scholar

[12] D. Kwon, M. Vannucci, J.J. Song and J. Jeong: Proteomics Vol. 8(2008), p.3019.

Google Scholar

[13] H.W. Ressom, R.S. Varghese, Z. Zhang and J. Xuan: Front Biosci Vol. 13(2008), p.691.

Google Scholar

[14] T.D. Pham: Int. J. Data Min. Bioin. Vol. 2(2008), p.405.

Google Scholar