With the rapid development of high-throughput techniques, mass spectrometry has been widely used for largescale protein analysis. To search for the existing proteins, discover biomarkers, and diagnose and prognose diseases, machine learning methods are applied in mass spectrometry data analysis. This paper reviews the applications of five kinds of
machine learning methods to mass spectrometry data analysis from an algorithmic point of view, including support vector
machine, decision tree, random forest, naive Bayesian classifier and deep learning.
Keywords: Mass spectrometry, high-throughput technique, machine learning, deep learning, computational proteomics, protein
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