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Diagnosis of Early Relapse in Ovarian Cancer Using Serum Proteomic Proflling

Diagnosis of Early Relapse in Ovarian Cancer Using Serum Proteomic Proflling,Jung Hun Oh,Jean Gao,Animesh Nandi,Prem Gurnani,Lynne Knowles,John Schorg

Diagnosis of Early Relapse in Ovarian Cancer Using Serum Proteomic Proflling   (Citations: 5)
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Surface-enhanced laser desorption/ionization time-of-∞ight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers to help early detection of the disease. Ovarian cancer commonly recurs at the rate of 75% within a few months or several years later after standard treatment. Since recurrent ovarian cancer is relatively di-cult to be diagnosed and small tumors generally respond better to treatment, new methods for the detection of early relapse in ovarian cancer are urgently needed. Here, we propose a new algorithm SVM-MB/RFE (SVM- Markov Blanket/Recursive Feature Elimination) based on SVM-RFE, which identifles biomarkers for predicting the early recurrence of ovarian cancer. In this approach, we flrst apply t-test for feature pruning and then binning using 5-fold cross validation. Finally, 58 peaks are obtained from 27000 of the raw data. Such dramatically reduced features relax the computational burden in the next step of our algorithm. We compare the performance of three feature selection algorithms and demonstrate that SVM-MB/RFE outperforms other methods.
Published in 2005.
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