Wavelet Transform and Bagging Predictor Approaches to Cancer Identification from Mass Spectrometry-based Proteomic Data
The early detection of cancer has the potential to dramatically reduce the mortality of cancer. Recently, using the mass spectrometry based proteomics to develop profiles of patient serum proteins, combined with bioinformatics algorithms has been reported as a promising method to achieve this goal. In this paper, we develop a workflow that combined wavelet transform, statistic analysis and bagging predictor to process a public ovarian cancer proteomic dataset, and ultimately obtained a discriminative proteomic pattern that can differentiate the cancer form control with high sensitivity and specificity. Compared with the previous studies, the results of our study are based on peaks of mass spectrometry and the discovered discriminative pattern is more biologically.
mass spectrometry proteomic wavelet transform bagging predictor
DU Jian-qiang WU Xiao-min WANG Bo SU Heng-jie MA Kai ZHANG Hu-qin
The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology,Xian Jiaotong University Xian 710049,P.R.China
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）