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Ovarian Carcinoma (OvCa) is the most lethal type of gynecological cancer. The studies show that about 90% patients could be saved if they are treated in the early stage. In this study, a novel biomarker selection approach is proposed which combines singular value decomposition (SVD) and Monte Carlo strategy to early OvCa detection. Other than supervised classification methods or differential expression...
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...
Many studies have been proposed to identify gene makers that are associated with cancers, but the found markers are approach dependent. For example, the results are correlated with classifiers in supervised feature selection, and many of them didn't consider the influences of other factors, such as the grades or stages of cancers. In this study, we proposed a supervised SVD approach to extract the...
Analyzing microarray data to identify interesting genes is a well-established methodology but often results in inconsistent conclusions and even fails because of the variations of experimental conditions. This study proposes an across factor normalization based singular value decomposition approach to microarray data analysis. The approach has been applied to analyze gene expression profiles to identify...
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