Li ZM, Zhuang X. Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer. Artif Intell Gastroenterol 2021; 2(4): 105-110 [DOI: 10.35712/aig.v2.i4.105]
Corresponding Author of This Article
Zhi-Ming Li, PhD, Academic Fellow, Tongji Medical College, Huazhong University of Science and Technology, Baofeng Street, Qiaokou District, Wuhan 430030, Hubei Province, China. lzmleo@xmu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Gastroenterol. Aug 28, 2021; 2(4): 105-110 Published online Aug 28, 2021. doi: 10.35712/aig.v2.i4.105
Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer
Zhi-Ming Li, Xuan Zhuang
Zhi-Ming Li, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Zhi-Ming Li, Xuan Zhuang, Department of Urology, The First Affiliated Hospital of Xiamen University, Xiamen 361003, Fujian Province, China
Xuan Zhuang, Department of Clinical Medicine, Fujian Medical University, Fuzhou 350122, Fujian Province, China
Author contributions: Li ZM conceptualized the paper; Li ZM and Zhuang X wrote the paper; all authors read and approved the final manuscript.
Supported byHealth Commission of Hubei Province Scientific Research Project, No. WJ2021Q023.
Conflict-of-interest statement: The authors declare that they have no competing interests to disclose.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhi-Ming Li, PhD, Academic Fellow, Tongji Medical College, Huazhong University of Science and Technology, Baofeng Street, Qiaokou District, Wuhan 430030, Hubei Province, China. lzmleo@xmu.edu.cn
Received: March 16, 2021 Peer-review started: March 16, 2021 First decision: April 15, 2021 Revised: April 22, 2021 Accepted: July 9, 2021 Article in press: July 9, 2021 Published online: August 28, 2021 Processing time: 166 Days and 12.5 Hours
Abstract
The microbiome has been identified as a causing factor for many cancers. Helicobacter pylori contributes to the development of gastric cancer (GC) and impacts disease treatments. The rapid development of sequencing technology is increasingly producing large-scale and complex big data. However, there are many obstacles in the analysis of these data by humans, which limit clinicians from making rapid decisions. Recently, the emergence of artificial intelligence (AI), including machine learning and deep learning, has greatly assisted clinicians in processing and interpreting large microbiome data. This paper reviews the application of AI in the study of the microbiome and discusses its potential in the diagnosis and therapy of GC. We also exemplify strategies for implementing microbiome-based precision medicines for patients with GC.
Core Tip: Artificial intelligence (AI) helps us understand the role of the microbiome in gastric cancer (GC) and further promote the development precision medicine. AI can be applied in the following three aspects: (1) AI improves the diagnostic accuracy for GC based on big data and gastric microbiome; (2) AI aids pathologists to diagnose gastric biopsies rapidly by sensitively detecting low abundance microbes; and (3) AI regulates individual’s dietary intake by giving new insight into host-microbiome interactions.
