Biomarker Discovery from Proteomic Data Based on Wavelet Package Transform and AdaBoost
Advances in proteomics provide a new method for early detection of cancer, it can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. In particular, proteomic pattern of body fluid has attracted attention as an approach to early detection of cancer. Mass spectrometry can provide rapid and precise measurements of the proteins in the body fluid. But the data processing is still a challenge due to noise artifact and high dimensionality of the proteomic data. In this paper, we proposed a scheme that combined wavelet package transform, statistic analysis and AdaBoost to process a public prostate cancer proteomic dataset, the obtained discriminative proteomic pattern can differentiate the cancer form control with high sensitivity and specificity.