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REVIEW article

Front. Microbiol.
Sec. Systems Microbiology
Volume 15 - 2024 | doi: 10.3389/fmicb.2024.1474078
This article is part of the Research Topic Artificial Intelligence in Pathogenic Microorganism Research View all 5 articles

Application of machine learning based genome sequence analysis in pathogen identification

Provisionally accepted
Gao Yunqiu Gao Yunqiu 1,2Min Liu Min Liu 1,3*
  • 1 Department of Dermatology, The First Affiliated Hospital of China Medical University, Shenyang, China
  • 2 Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, shenyang, China
  • 3 Institute of Respiratory Disease, First Hospital of China Medical University, Shenyang, Liaoning Province, China

The final, formatted version of the article will be published soon.

    Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combiningRaman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 minutes.These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics.In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

    Keywords: Artificial intelligence (AI), antibiotic resistance, Pathogenic microorganisms, Machine Learning (ML), diagnosis

    Received: 01 Aug 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Yunqiu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Min Liu, Department of Dermatology, The First Affiliated Hospital of China Medical University, Shenyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.