ArticleFrontiers in microbiology2021
Machine Learning Predicts the Presence of 2,4,6-Trinitrotoluene in Sediments of a Baltic Sea Munitions Dumpsite Using Microbial Community Compositions.
Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Predicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning.Microbiology spectrum · 2026Article
- Enhancing the understanding of environmental microbiomes through topic modeling: a quantitative and qualitative analysis.Environmental microbiome · 2026Article
- A benchmark analysis of feature selection and machine learning methods for environmental metabarcoding datasets.Computational and structural biotechnology journal · 2025Article
- Exploring and exploiting the rice phytobiome to tackle climate change challenges.Plant communications · 2024Review
- Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges.Frontiers in microbiology · 2022Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacteria are ubiquitous and live in complex microbial communities. Due to differences in physiological properties and niche preferences among community members, microbial communities respond in specific ways to environmental drivers, potentially resulting in distinct microbial fingerprints for a given environmental state. As proof of the principle, our goal was to assess the opportunities and limitations of machine learning to detect microbial fingerprints indicating the presence of the munition compound 2,4,6-trinitrotoluene (TNT) in southwestern Baltic Sea sediments. Over 40 environmental variables including grain size distribution, elemental composition, and concentration of munition compounds (mostly at pmol⋅g
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