气候变化领域集成服务门户(CCPortal): An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

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DOI	10.1016/j.marpolbul.2019.110530
	An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts
	Janßen R.; Zabel J.; von Lukas U.; Labrenz M.
发表日期	2019
ISSN	0025326X
卷号	149
英文摘要	Machine learning algorithms can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network in comparison to a Random Forest model to detect induced changes in microbial communities, in order to support environmental monitoring efforts of contamination events. Models were trained on taxon count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the taxa primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species for glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data. © 2019 Elsevier Ltd
英文关键词	ANN; Baltic Sea; Glyphosate; Microbial community composition; Monitoring; NGS
语种	英语
scopus关键词	Decision trees; Forestry; Herbicides; Machine learning; Microorganisms; Monitoring; Random forests; RNA; Baltic sea; Contamination events; Environmental Monitoring; Glyphosates; Microbial communities; Microbial community composition; Random forest modeling; Reduction of inputs; Neural networks; glyphosate; RNA 16S; glycine; glyphosate; RNA 16S; artificial neural network; biomonitoring; community composition; data set; estuarine environment; glyphosate; microbial community; RNA; Ahrensia; Aminobacter; amplicon; Article; artificial neural network; bacterium; Caulobacter; community succession; controlled study; Dokdonella; environmental monitoring; Ferrovibrio; Gallaecimonas; Hyphomonas; Idiomarina; Limnohabitans; Loktanella; Massilia; microbial community; microcosm; Nesiotobacter; next generation sequencing; nonhuman; Parvibaculum; predictive value; random forest; Reyranella; Rhizobium; Sphingomonas; Sphingopyxis; Thalassobaculum; water contamination; algorithm; drug effect; genetics; high throughput sequencing; machine learning; microbiology; microflora; randomization; toxicity; water pollutant; Algorithms; Environmental Monitoring; Glycine; High-Throughput Nucleotide Sequencing; Machine Learning; Microbiota; Neural Networks, Computer; Random Allocation; RNA, Ribosomal, 16S; Water Microbiology; Water Pollutants, Chemical
来源期刊	Marine Pollution Bulletin
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/149572
作者单位	Biological Oceanography, Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Mecklenburg-Western Pomerania, Germany; Maritime Graphics, Fraunhofer Institute for Computer Graphics Research, Rostock, Mecklenburg-Western Pomerania, Germany
推荐引用方式 GB/T 7714	Janßen R.,Zabel J.,von Lukas U.,et al. An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts[J],2019,149.
APA	Janßen R.,Zabel J.,von Lukas U.,&Labrenz M..(2019).An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts.Marine Pollution Bulletin,149.
MLA	Janßen R.,et al."An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts".Marine Pollution Bulletin 149(2019).