Selecting Biomarkers for Ovarian Cancer Detection Using SVD and Monte Carlo Methods

Ovarian cancer (OvCa) has become one of the most lethal gynecological cancers in the world. The identification of ovarian cancer linked biomarkers will provide the basis of diagnoses and treatment. In this study, we proposed to combine singular value decomposition (SVD) and Monte Carlo method to analyze the OvCa data and predict the outcomes of samples. A supervised SVD was proposed to weight biomarkers according to their relative importance in sample clustering, and the candidate biomarkers were selected. Biomarkers were further selected with Monte Carlo method from candidate biomarkers over different classifiers. With the selected biomarkers, more than 90% classification accuracy was achieved over classifiers. These results are also supported by independent biological studies.