Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection.

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.

journal_name

Acta Pharmacol Sin

authors

Hong YJ,Wang XD,Shen D,Zeng S

doi

10.1111/j.1745-7254.2008.00861.x

subject

Has Abstract

pub_date

2008-10-01 00:00:00

pages

1240-6

issue

10

eissn

1671-4083

issn

1745-7254

journal_volume

29

pub_type

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