Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection

被引：12
作者：
Hong Y.-J. [1 ]
Wang X.-D. [1 ]
Shen D. [2 ]
Zeng S. [1 ]
机构：
[1] College of Pharmaceutical Sciences, Zhejiang University
[2] Alps Biostatistics Services LLC, Philadelphia, PA
来源：
Acta Pharmacologica Sinica | 2008年 / 29卷 / 10期
关键词：
Discrimination analysis; Mass spectrometry; Ovarian cancer; Proteomics;
D O I：
10.1111/j.1745-7254.2008.00861.x
中图分类号：
学科分类号：
摘要：
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. © 2008 CPS and SIMM.
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页码：1240 / 1246
页数：6
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