Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy
Notice bibliographique
Résumé
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 Aminicenantia class (within the Acidobacteriota phylum) and the Sulfurovum genus (within the Campylobacterota phylum) were most diagnostic for the presence of low molecular weight hydrocarbon gases. The Campylobacterota lineage was also important in Scotian Slope sediments, along with sequences affiliated with the class-level JS1 group (within the Caldatribacteriota phylum) for determining hydrocarbon-positive sites. Testing these models in geographically distant seafloor basins showed that the microbial communities between basins varied sufficiently to prevent consistently accurate reciprocal predictions. However, models trained on a combined data set and filtered for important features performed substantially better, supporting the feasibility of generalized models under stringent feature selection. These results highlight the potential of seabed microbial taxonomy-based hydrocarbon seep site prediction when paired with refined sampling and consistent geochemical characterization. IMPORTANCE Our study showcases an important use of bioinformatics in an interdisciplinary context, by combining hydrocarbon geochemistry and microbial biodiversity DNA sequencing profiles. We trained and compared different machine learning models on 16S rRNA-based bacterial taxonomy data using 377 DNA sequencing libraries from marine surface sediments in two different hydrocarbon prospective marine basins from different parts of the global ocean to predict the hydrocarbon status of sediment samples. Of all algorithms tested, Gradient Boosting Machines worked best for this objective. Feature importance scores from the models highlighted that in Gulf of Mexico samples, members of the Aminicenantales order and Campylobacterota lineages were most diagnostic for the presence of low molecular weight hydrocarbon gases. The Campylobacterota lineage was also important in NW Atlantic Scotian Slope sediments, along with sequences affiliated with the class-level group JS1 (within the Caldatribacteriota phylum) for determining hydrocarbon-positive sites, though several features appeared to be basin-specific. Importantly, models had a high prediction accuracy when predicting samples from the same basin but were less effective in predicting the hydrocarbon status in reciprocal basin testing, pointing to the ecological differences in hydrocarbon-driven environmental selection in different parts of the ocean. However, combined models using a refined set of predictive features improved cross-basin performance, highlighting the feasibility of a broader application. Results highlight the exciting potential of microbial taxonomy-based machine learning models in predicting broader ecological, oceanographic, and geological phenomena at the biosphere-geosphere interface.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».