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Current Proteomics

Editor-in-Chief

ISSN (Print): 1570-1646
ISSN (Online): 1875-6247

Review Article

Machine Learning for Mass Spectrometry Data Analysis in Proteomics

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

Volume 18, Issue 5, 2021

Published on: 23 October, 2020

Page: [620 - 634] Pages: 15

DOI: 10.2174/1570164617999201023145304

Price: $65

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.

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

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