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

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Bibliographic Details
Title: Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy.
Authors: Khan R; Geomicrobiology Group, Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada., Bhardwaj T; Geomicrobiology Group, Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada., Li C; Geomicrobiology Group, Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada., Chakraborty A; Department of Biological Sciences, Idaho State University, Pocatello, Idaho, USA., Seoane JM; Repsol SA, Madrid, Spain., Brooks JM; TDI-Brooks International, College Station, Texas, USA., Bernard BB; TDI-Brooks International, College Station, Texas, USA., MacDonald A; Natural Resources Canada, Geological Survey of Canada Atlantic, Dartmouth, Canada., MacAdam N; Natural Resources Canada, Geological Survey of Canada Atlantic, Dartmouth, Canada., Campbell C; Nova Scotia Department of Natural Resources and Renewables, Government of Nova Scotia, Halifax, Canada., Fowler M; Applied Petroleum Technology Canada, Calgary, Alberta, Canada., Hubert CRJ; Geomicrobiology Group, Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada.
Source: Microbiology spectrum [Microbiol Spectr] 2025 Oct 07; Vol. 13 (10), pp. e0303324. Date of Electronic Publication: 2025 Aug 20.
Publication Type: Journal Article
Journal Info: Publisher: ASM Press Country of Publication: United States NLM ID: 101634614 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2165-0497 (Electronic) Linking ISSN: 21650497 NLM ISO Abbreviation: Microbiol Spectr Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2165-0497
DOI:10.1128/spectrum.03033-24