Application of diffusion based framelet transform to the MS-based proteomics data preprocessing

Mass Spectrometry (MS) is one of the main detection tools for high-throughput proteomics. The preprocessing of mass spectra is fundamental for its successive examination like biomarker detection or protein identification. Peaks are extracted from a data set for biomarker identification. Biomarkers are useful for differentiating diseased and normal samples. Framelet transform has gradually become one of the important methodologies in the MS data preprocessing. The smoothing and baseline removal are important steps of the preprocessing of mass spectra. Nonlinear diffusion method has been effectively used in removing unimportant, minor variations while keeping vital features such as discontinuities. This paper reviews the application of diffusion based framelet transform in preprocessing stages for smoothing and peak detection of MS data.