Machine learning reveals genes impacting oxidative stress resistance across yeasts

Dataset · Fri Apr 18 04:00:00 EDT 2025

DOI:https://doi.org/10.25345/C5WH2DS6P· OSTI ID:3003641

Coon, Joshua J. ^[1]

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1. GLBRC - University of Wisconsin

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.

• Research Organization: Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• DOE Contract Number: SC0018409
• OSTI ID: 3003641
• Country of Publication: United States
• Language: English

Related Subjects

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