With the rapid development of high-throughput techniques, mass spectrometry has been
widely used for large-scale 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.