Combining MALDI-MS with machine learning for metabolomic characterization of lung cancer patient sera-Reference-Cited by-同舟云学术

Combining MALDI-MS with machine learning for metabolomic characterization of lung cancer patient sera

Published:2022 Issue:5 Volume:14 Page:499-507
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Lai Xiaopin¹,Guo Kunbin²,Huang Wei¹^ORCID,Su Yang¹,Chen Siyu¹,Li Qiongdan¹,Liang Kaiqing¹,Gao Wenhua¹,Wang Xin²,Chen Yuping²,Wang Hongbiao²,Lin Wen²,Wei Xiaolong²,Ni Wenxiu³^ORCID,Lin Yan⁴,Jiang Dazhi⁵,Cheng Yu-Hong⁶^ORCID,Che Chi-Ming⁶^ORCID,Ng Kwan-Ming¹^ORCID

Affiliation:

1. Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China

2. Cancer Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China

3. Department of Medical Chemistry, Shantou University Medical College, Shantou, Guangdong, 515041, P. R. China

4. The Second Affiliated Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China

5. Department of Computer Science, College of Engineering, Shantou University, Guangdong, 515063, P. R. China

6. Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong S. A. R., P. R. China

Abstract

This work establishes an integrated MALDI-MS data processing approach combined with machine learning methods for metabolomic characterization of lung cancer, demonstrating its potential for widespread application in biomarker screening.

Funder

National Natural Science Foundation of China

Shantou University

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Chemical Engineering,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/AY/D1AY01940F

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