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Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection

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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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Keywords: discrimination analysis; mass spectrometry; ovarian cancer; proteomics

Document Type: Research Article

Affiliations: 1: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China 2: Alps Biostatistics Services LLC, Philadelphia, Pennsylvania, USA

Publication date: 2008-10-01

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