Preprocessing and Classifying of Mass Spectrometry-Based Proteomics Data Using Wavelet Transform and Decision Tree Learning
Advances in mass spectrometry-based proteomics have brought expectations for biomedical researchers. It can be used for identify proteomic patterns in body fluids to discriminate patients from control, the results are inspiring. However, most of the earlier studies are based on the direct application of original MS data, together with dimension reduction or feature selection methods. We deemed that only the peaks of MS data have real biological meaning, so it's important to obtain the ultimate proteomic pattern using the real peaks. In this paper, we proposed a workflow that combined wavelet transform, statistical analysis and decision tree learning to process MS data. Especially, the statistical analysis which have not been attached too much importance in most studies was investigated, the possible distribution law of the MS peaks was proposed.