Machine learning reveals genes impacting oxidative stress resistance across yeasts

Coon, Joshua J.

doi:10.25345/C5WH2DS6P

Title: Machine learning reveals genes impacting oxidative stress resistance across yeasts

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Abstract

Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organisms, including host infection. Here, we characterized the variation in resistance to ROS across the ancient yeast subphylum Saccharomycotina and used machine learning (ML) to identify gene families whose sizes were predictive of ROS resistance.

Authors:

Coon, Joshua J.

GLBRC - University of Wisconsin

Publication Date:: Fri Apr 18 04:00:00 UTC 2025

DOE Contract Number:: SC0018409

Research Org.:: Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Subject:: AI; Artificial Intelligece; Data Independent Acquisition; DatasetType:Proteomics; Kluyveromyces lactis; Machine Learning; Oxidative Stress; Proteomics; Reactive Oxygen Species; Saccharomyces cerevisiae; Yeast

OSTI Identifier:: 3003641

DOI:: https://doi.org/10.25345/C5WH2DS6P

Citation Formats


                    Coon, Joshua J. Machine learning reveals genes impacting oxidative stress resistance across yeasts.  United States: N. p., 2025. 
        Web.  doi:10.25345/C5WH2DS6P.

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                    Coon, Joshua J. Machine learning reveals genes impacting oxidative stress resistance across yeasts.  United States.  doi:https://doi.org/10.25345/C5WH2DS6P

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                    Coon, Joshua J. 2025.  
"Machine learning reveals genes impacting oxidative stress resistance across yeasts".  United States.  doi:https://doi.org/10.25345/C5WH2DS6P.  https://www.osti.gov/servlets/purl/3003641. Pub date:Fri Apr 18 04:00:00 UTC 2025

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@article{osti_3003641,

  title        = {Machine learning reveals genes impacting oxidative stress resistance across yeasts},

  author       = {Coon, Joshua J.},

  abstractNote = {Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organisms, including host infection. Here, we characterized the variation in resistance to ROS across the ancient yeast subphylum Saccharomycotina and used machine learning (ML) to identify gene families whose sizes were predictive of ROS resistance.},

  doi          = {10.25345/C5WH2DS6P},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Apr 18 04:00:00 UTC 2025},

  month        = {Fri Apr 18 04:00:00 UTC 2025}

}

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DOI: https://doi.org/10.25345/C5WH2DS6P

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