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.
ovarian cancer SVD Monte Carlo method biomarker
Haifeng Lai Bin Han Lei Zhu Yan Chen Lihua Li Rebecca Sutphen
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）