A Supervised SVD Approach to Ovarian Cancer Chemotherapy Response Prediction with Across Factor Normalization

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 gene features linked to chemotherapy response patients of ovarian cancer, and applied across factor normalization to remove the influences of the factors. Chi square test is used to detect whether the factors affect the distribution of chemotherapy response and quantile-quantile plot is used to detect the distribution of chemotherapy response samples. The experimental results show that the influences of the factors are removed effectively, and the classification performance of gene markers selected by the proposed methods outperform that by SVMRFE and T-test in seven classifiers except for JRip classifier and NaiveBayes classifier.