Abstract
Aim:
A discrimination analysis has been explored for the probabilistic classification of healthy versus ovarian cancer serum samples using proteomics data from mass spectrometry (MS).
Methods:
The method employs data normalization, clustering, and a linear discriminant analysis on surface-enhanced laser desorption ionization (SELDI) time-of-flight MS data. The probabilistic classification method computes the optimal linear discriminant using the complex human blood serum SELDI spectra. Cross-validation and training/testing data-split experiments are conducted to verify the optimal discriminant and demonstrate the accuracy and robustness of the method.
Results:
The cluster discrimination method achieves excellent performance. The sensitivity, specificity, and positive predictive values are above 97% on ovarian cancer. The protein fraction peaks, which significantly contribute to the classification, can be available from the analysis process.
Conclusion:
The discrimination analysis helps the molecular identities of differentially expressed proteins and peptides between the healthy and ovarian patients.
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This project was supported by the Zhejiang Provincial Science and Technology Foundation of China (No 2005C13026).
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Hong, Yj., Wang, Xd., Shen, D. et al. Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection. Acta Pharmacol Sin 29, 1240–1246 (2008). https://doi.org/10.1111/j.1745-7254.2008.00861.x
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DOI: https://doi.org/10.1111/j.1745-7254.2008.00861.x
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