Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy.

ABSTRACT: Hydrocarbon seepage in marine sediments exerts selective pressure on benthic microbiomes. Accordingly, microbial community composition in these sediments can reflect the presence of hydrocarbons, with specific groups being more prolific in association with seepage. Here, we tested machine learning models with large 16S rRNA gene amplicon data sets derived from marine sediments in deep-sea hydrocarbon prospective areas of the Eastern Gulf of Mexico and NW Atlantic Scotian Slope. Utilizing H2O's AutoML machine learning platform, it was determined that Gradient Boosting Machines performed best for creating 16S rRNA-based models that successfully predict the presence of hydrocarbons. Feature importance scores from the models revealed that in Gulf of Mexico samples, members of the Aminicenan

SUBMITTER: Khan R 

PROVIDER: S-EPMC12502611 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature