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 didnt 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.
Chi Square test Across factor normalization SVD Quantile-Quantile plot Chemotherapy response
Yan Chen Bin Han Lei Zhu Weiwei Wang Lihua Li Jonathan M.Lancaster
Institute for Biomedical Engineering and Instrumentation Hangzhou Dianzi University Hangzhou,P.R.Chi H.Lee Moffitt Cancer Center & Research Institute University of South Florida Tampa,Florida,USA
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）