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.