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Machine Learning for Mass Spectrometry Data Analysis in Proteomics

[ Vol. 18 , Issue. 5 ]


Juntao Li, Kanglei Zhou* and Bingyu Mu*   Pages 620 - 634 ( 15 )


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.


Mass spectrometry, high-throughput technique, machine learning, deep learning, computational proteomics, protein identification.


College of Mathematics and Information Science, Henan Normal University, Xinxiang, School of Computer Science and Engineering, Beihang University, Beijing, College of Arts and Design, Zhengzhou University of Light Industry, Zhengzhou

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