Research on Serum Spectrum Analysis Model Applied to Pathema Identification

Yan Kun Li; Shuo Yang

doi:10.4028/www.scientific.net/AMR.864-867.486

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 864-867Research on Serum Spectrum Analysis Model Applied...

Research on Serum Spectrum Analysis Model Applied to Pathema Identification

Abstract:

The SELDI-TOF MS serum peptide profiles of normal and malignant tumor samples were studied by pattern recognition method. In this study, Partial Least Squares-Discriminate Analysis (PLS-DA) combined with consensus classification model was constructed to predict practical serum samples and compared with the results of principal component analysis (PCA) method. The correctness of consensus PLS-DA classification model for normal and malignant samples was 90% and 84% respectively. So the approach proposed was proved to be a reliable and practicable method for cancer identification.

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Periodical:

Advanced Materials Research (Volumes 864-867)

Pages:

486-489

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.864-867.486

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Online since:

December 2013

Authors:

Yan Kun Li, Shuo Yang

Keywords:

Classification, Consensus, LS-DA, Serum Peptide Profile

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