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Call for papers - Artificial intelligence and machine learning applications in microbiology

Guest Editors

Edoardo Pasolli, PhD, University of Naples Federico II, Italy
Bei-Wen Ying, PhD, University of Tsukuba, Japan

Submission Status: Open   |   Submission Deadline: 31 July 2025

BMC Microbiology launches the collection Artificial intelligence and machine learning applications in microbiology. We welcome submissions focusing on the application of artificial intelligence and machine learning to microorganisms and microbial interactions, covering topics such as microbial ecology, microbiome analysis, microbial interactions, microbial genomics and metagenomics, microbial synthetic biology and metabolic engineering, antimicrobial resistance, and pathogen detection and clinical diagnostics. Research mainly focusing on AI and ML tools or methodological advances without a clear focus on microorganisms and/or microbial interactions will not be considered.

New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health & Well-Being.

Meet the Guest Editors

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Edoardo Pasolli, PhD, University of Naples Federico II, Italy

Dr Pasolli is an Associate Professor at the University of Naples Federico II, Department of Agricultural Sciences, Italy. His research focuses on developing and applying computational tools and machine learning methods to study complex ecosystems, particularly within human and food microbiomes.


Bei-Wen Ying, PhD, University of Tsukuba, Japan

Dr Ying is currently an Associate Professor at the University of Tsukuba, Japan. In the early stages of her career at the University of Tokyo (UT) and at CNRS (Centre national de la recherche scientifique), France she focused on molecular biology, specifically protein biosynthesis. After transitioning to the Computational Science Department at Osaka University, she began multidisciplinary research on genome rewiring and evolution. Since establishing her lab at the University of Tsukuba in 2016, she has been a pioneer in integrating machine learning into bacterial growth analysis. Dr Ying now consistently combines machine learning with research in both bacterial and mammalian cell cultures.

About the Collection

Understanding the complexity of microbial organisms and ecosystems, as well as the molecular mechanisms underlying microbial processes and interactions with other (micro)organisms or the environment, requires the data analysis and mining of large datasets. Applying artificial intelligence (AI) and machine learning (ML) to address microbiology questions is expected to grow and impact the field substantially, offering unprecedented opportunities for revealing how microbial organisms and microbiomes work. AI and ML-assisted technologies can enable researchers to analyze very complex and multivariate datasets, for instance, generated by multi-omics or single-cell techniques, and study, e.g., how microorganisms interact in their natural environment, the molecular basis of antimicrobial resistance, the identification of novel drug targets or vaccine candidates, or improve pathogen diagnosis and treatment of infectious diseases. Moreover, AI and ML can be used to make predictions of future outcomes, behaviors, and trends based on existing data (predictive analytics) that can be validated experimentally.

Advances in this emerging research area promise innovative solutions to tackle current challenges in microbiology, with implications for public health and the environment. Therefore, in support of United Nations’ Sustainable Development Goal 3 (SDG 3, Good Health and Well-Being), BMC Microbiology launches the collection Artificial intelligence and machine learning applications in microbiology. This collection invites researchers to submit their work on the applications and integration of AI and ML technologies in microbiology. Research mainly focusing on AI and ML tools or methodological advances without a clear focus on microorganisms and/or microbial interactions will not be considered. We invite researchers and experts in the field to submit research articles covering a broad range of topics including, but not limited to:

  • ML and AI applications in microbial ecology and microbiome analysis  
  • ML and deep learning applications to understand the mechanisms of microbial interactions
  • AI and ML techniques applied in microbial systems biology and population dynamics
  • AI and  ML applications for synthetic biology and metabolic engineering
  • AI and ML-assisted multi-omics and single-cell analysis
  • ML applications for microbial genomics and metagenomics
  • Deep learning applications and techniques for imaging microorganisms   
  • AI in the diagnosis of microorganisms causing infectious diseases
  • AI and ML applications to investigate antimicrobial resistance
  • AI and ML in microbial therapeutics and drug discovery (e.g., design of antibiotic compounds, development of vaccines)
  • AI and ML in pathogen detection and clinical diagnostics  
  • AI models to predict future infectious disease outbreaks


Image credit: © NicoElNino / Getty Images / iStock

  1. Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early identification of infectious agents and determination of drug-resistant...

    Authors: Xiaobo Xu, Zhaofeng Wang, Erjie Lu, Tao Lin, Hengchao Du, Zhongfei Li and Jiahong Ma
    Citation: BMC Microbiology 2025 25:44
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Artificial intelligence and machine learning applications in microbiology" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.