Machine Learning for Mass Spectrometry Data Analysis in Proteomics

Title:Machine Learning for Mass Spectrometry Data Analysis in Proteomics

Volume: 18 Issue: 5

Author(s): Juntao Li, Kanglei Zhou* Bingyu Mu*

Affiliation:

• School of Computer Science and Engineering, Beihang University, Beijing,China

• College of Arts and Design, Zhengzhou University of Light Industry, Zhengzhou,China

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

Abstract: 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.

Cite this article as:

Li Juntao , Zhou Kanglei*, Mu Bingyu *, Machine Learning for Mass Spectrometry Data Analysis in Proteomics, Current Proteomics 2021; 18 (5) . https://dx.doi.org/10.2174/1570164617999201023145304