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Issue 1, 2016
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Serum SELDI-TOF MS analysis model applied to benign and malignant ovarian tumor identification

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Abstract

SELDI-TOF MS serum peptide profiles of malignant and benign ovarian tumor samples were studied using a pattern recognition technique. The model of uncorrelated linear discriminant analysis (ULDA) combined with variables selection method of variance analysis was constructed to identify ovarian tumor serum samples and compared with the results obtained from principal component analysis (PCA) and partial least squares-discriminate analysis (PLS-DA). In addition, special peaks (m/z locations) as potential biomarkers were selected in this study. The good results indicate that the strategy of ULDA combined with variables selection applied to serum SELDI-TOF MS is a practicable and promising method for the ovarian malignant and benign tumor identification and selection of potential biomarkers.

Graphical abstract: Serum SELDI-TOF MS analysis model applied to benign and malignant ovarian tumor identification

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Article information


Submitted
21 Sep 2015
Accepted
10 Nov 2015
First published
16 Nov 2015

Anal. Methods, 2016,8, 183-188
Article type
Paper

Serum SELDI-TOF MS analysis model applied to benign and malignant ovarian tumor identification

Y. Li and X. Zeng, Anal. Methods, 2016, 8, 183
DOI: 10.1039/C5AY02517F

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