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EDITORIAL article

Front. Microbiol.
Sec. Systems Microbiology
Volume 15 - 2024 | doi: 10.3389/fmicb.2024.1451243
This article is part of the Research Topic Combining Machine Learning, Computational Modeling, and High Throughput Experimentation to Accelerate Discovery in Systems Microbiology View all 5 articles

Combining Machine Learning, Computational Modeling, and High Throughput Experimentation to Accelerate Discovery in Systems Microbiology

Provisionally accepted
Miguel Fuentes-Cabrera Miguel Fuentes-Cabrera 1*Paolo Zuliani Paolo Zuliani 2Bowen Li Bowen Li 3Jared Wilmoth Jared Wilmoth 4Aivett Bilbao Aivett Bilbao 5Alice C. Dohnalkova Alice C. Dohnalkova 5
  • 1 Khoury College of Computer Sciences, Northeastern University, Boston, United States
  • 2 Sapienza University of Rome, Rome, Lazio, Italy
  • 3 Newcastle University, Newcastle upon Tyne, North East England, United Kingdom
  • 4 University of Maryland, College Park, College Park, Maryland, United States
  • 5 Pacific Northwest National Laboratory (DOE), Richland, Washington, United States

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

    Keywords: machine learning, Microbiology, high-throughput experiments, computational simulations, accelerate discovery

    Received: 18 Jun 2024; Accepted: 24 Jun 2024.

    Copyright: © 2024 Fuentes-Cabrera, Zuliani, Li, Wilmoth, Bilbao and Dohnalkova. 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: Miguel Fuentes-Cabrera, Khoury College of Computer Sciences, Northeastern University, Boston, United States

    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.