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Author:

Zhao Xin-Yuan (Zhao Xin-Yuan.) (Scholars:赵欣苑) | Qin Wei-Jie (Qin Wei-Jie.) | Qian Xiao-Hong (Qian Xiao-Hong.)

Indexed by:

Scopus SCIE PKU CSCD

Abstract:

Deep learning is the most popular research area in the field of machine learning in recent years, especially in image and speech recognition, natural language processing, and automatic driving. Biological mass spectrometry is an important research tool in the field of life sciences and plays a key role in proteomics, metabolomics, and biopharmaceuticals. In recent years, based on the development of deep learning methods, the big data analysis in proteomics centered on biological mass spectrometry will usher into a new era. This article reviews the latest applications of deep learning methods in the analysis of biological mass spectrometry data and proteomics research.

Keyword:

proteomics big data biological mass spectrometry deep learning

Author Community:

  • [ 1 ] [Zhao Xin-Yuan]Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing 100124, Peoples R China
  • [ 2 ] [Qin Wei-Jie]Beijing Inst Lifeom, Beijing Proteome Res Ctr, State Key Lab Prote, Natl Ctr Prot Sci Beijing, Beijing 102206, Peoples R China
  • [ 3 ] [Qian Xiao-Hong]Beijing Inst Lifeom, Beijing Proteome Res Ctr, State Key Lab Prote, Natl Ctr Prot Sci Beijing, Beijing 102206, Peoples R China

Reprint Author's Address:

  • [Qin Wei-Jie]Beijing Inst Lifeom, Beijing Proteome Res Ctr, State Key Lab Prote, Natl Ctr Prot Sci Beijing, Beijing 102206, Peoples R China;;[Qian Xiao-Hong]Beijing Inst Lifeom, Beijing Proteome Res Ctr, State Key Lab Prote, Natl Ctr Prot Sci Beijing, Beijing 102206, Peoples R China

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Source :

PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS

ISSN: 1000-3282

Year: 2018

Issue: 12

Volume: 45

Page: 1214-1223

0 . 3 0 0

JCR@2022

ESI Discipline: BIOLOGY & BIOCHEMISTRY;

ESI HC Threshold:193

JCR Journal Grade:4

Cited Count:

WoS CC Cited Count: 4

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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