An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

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 moni...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Janßen, René [verfasserIn] Zabel, Jakob [verfasserIn] von Lukas, Uwe [verfasserIn] Labrenz, Matthias [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	ANN Glyphosate NGS Monitoring Microbial community composition Baltic Sea

Übergeordnetes Werk:	Enthalten in: Marine pollution bulletin - Amsterdam [u.a.] : Elsevier Science, 1970, 149
Übergeordnetes Werk:	volume:149

DOI / URN:	10.1016/j.marpolbul.2019.110530

Katalog-ID:	ELV003199975

Internformat


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245	1	0	\|a An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts
264		1	\|c 2019
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
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520			\|a 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.
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650		4	\|a Glyphosate
650		4	\|a NGS
650		4	\|a Monitoring
650		4	\|a Microbial community composition
650		4	\|a Baltic Sea
700	1		\|a Zabel, Jakob \|e verfasserin \|4 aut
700	1		\|a von Lukas, Uwe \|e verfasserin \|4 aut
700	1		\|a Labrenz, Matthias \|e verfasserin \|0 (orcid)0000-0003-3452-8631 \|4 aut
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matchkey_str	article:18793363:2019----::nriiilerlewradadmoetdniylpoaematdrcihomntebsd
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publishDate	2019
allfields	10.1016/j.marpolbul.2019.110530 doi (DE-627)ELV003199975 (ELSEVIER)S0025-326X(19)30668-X DE-627 ger DE-627 rda eng 550 333.7 DE-600 BIODIV DE-30 fid 38.90 bkl 43.50 bkl Janßen, René verfasserin (orcid)0000-0002-0605-6860 aut An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. ANN Glyphosate NGS Monitoring Microbial community composition Baltic Sea Zabel, Jakob verfasserin aut von Lukas, Uwe verfasserin aut Labrenz, Matthias verfasserin (orcid)0000-0003-3452-8631 aut Enthalten in Marine pollution bulletin Amsterdam [u.a.] : Elsevier Science, 1970 149 Online-Ressource (DE-627)320410897 (DE-600)2001296-2 (DE-576)259484415 1879-3363 nnns volume:149 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.90 Ozeanologie Ozeanographie 43.50 Umweltbelastungen AR 149
spelling	10.1016/j.marpolbul.2019.110530 doi (DE-627)ELV003199975 (ELSEVIER)S0025-326X(19)30668-X DE-627 ger DE-627 rda eng 550 333.7 DE-600 BIODIV DE-30 fid 38.90 bkl 43.50 bkl Janßen, René verfasserin (orcid)0000-0002-0605-6860 aut An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. ANN Glyphosate NGS Monitoring Microbial community composition Baltic Sea Zabel, Jakob verfasserin aut von Lukas, Uwe verfasserin aut Labrenz, Matthias verfasserin (orcid)0000-0003-3452-8631 aut Enthalten in Marine pollution bulletin Amsterdam [u.a.] : Elsevier Science, 1970 149 Online-Ressource (DE-627)320410897 (DE-600)2001296-2 (DE-576)259484415 1879-3363 nnns volume:149 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.90 Ozeanologie Ozeanographie 43.50 Umweltbelastungen AR 149
allfields_unstemmed	10.1016/j.marpolbul.2019.110530 doi (DE-627)ELV003199975 (ELSEVIER)S0025-326X(19)30668-X DE-627 ger DE-627 rda eng 550 333.7 DE-600 BIODIV DE-30 fid 38.90 bkl 43.50 bkl Janßen, René verfasserin (orcid)0000-0002-0605-6860 aut An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. ANN Glyphosate NGS Monitoring Microbial community composition Baltic Sea Zabel, Jakob verfasserin aut von Lukas, Uwe verfasserin aut Labrenz, Matthias verfasserin (orcid)0000-0003-3452-8631 aut Enthalten in Marine pollution bulletin Amsterdam [u.a.] : Elsevier Science, 1970 149 Online-Ressource (DE-627)320410897 (DE-600)2001296-2 (DE-576)259484415 1879-3363 nnns volume:149 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.90 Ozeanologie Ozeanographie 43.50 Umweltbelastungen AR 149
allfieldsGer	10.1016/j.marpolbul.2019.110530 doi (DE-627)ELV003199975 (ELSEVIER)S0025-326X(19)30668-X DE-627 ger DE-627 rda eng 550 333.7 DE-600 BIODIV DE-30 fid 38.90 bkl 43.50 bkl Janßen, René verfasserin (orcid)0000-0002-0605-6860 aut An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. ANN Glyphosate NGS Monitoring Microbial community composition Baltic Sea Zabel, Jakob verfasserin aut von Lukas, Uwe verfasserin aut Labrenz, Matthias verfasserin (orcid)0000-0003-3452-8631 aut Enthalten in Marine pollution bulletin Amsterdam [u.a.] : Elsevier Science, 1970 149 Online-Ressource (DE-627)320410897 (DE-600)2001296-2 (DE-576)259484415 1879-3363 nnns volume:149 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.90 Ozeanologie Ozeanographie 43.50 Umweltbelastungen AR 149
allfieldsSound	10.1016/j.marpolbul.2019.110530 doi (DE-627)ELV003199975 (ELSEVIER)S0025-326X(19)30668-X DE-627 ger DE-627 rda eng 550 333.7 DE-600 BIODIV DE-30 fid 38.90 bkl 43.50 bkl Janßen, René verfasserin (orcid)0000-0002-0605-6860 aut An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. ANN Glyphosate NGS Monitoring Microbial community composition Baltic Sea Zabel, Jakob verfasserin aut von Lukas, Uwe verfasserin aut Labrenz, Matthias verfasserin (orcid)0000-0003-3452-8631 aut Enthalten in Marine pollution bulletin Amsterdam [u.a.] : Elsevier Science, 1970 149 Online-Ressource (DE-627)320410897 (DE-600)2001296-2 (DE-576)259484415 1879-3363 nnns volume:149 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.90 Ozeanologie Ozeanographie 43.50 Umweltbelastungen AR 149
language	English
source	Enthalten in Marine pollution bulletin 149 volume:149
sourceStr	Enthalten in Marine pollution bulletin 149 volume:149
format_phy_str_mv	Article
bklname	Ozeanologie Ozeanographie Umweltbelastungen
institution	findex.gbv.de
topic_facet	ANN Glyphosate NGS Monitoring Microbial community composition Baltic Sea
dewey-raw	550
isfreeaccess_bool	false
container_title	Marine pollution bulletin
authorswithroles_txt_mv	Janßen, René @@aut@@ Zabel, Jakob @@aut@@ von Lukas, Uwe @@aut@@ Labrenz, Matthias @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	320410897
dewey-sort	3550
id	ELV003199975
language_de	englisch
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author	Janßen, René
spellingShingle	Janßen, René ddc 550 fid BIODIV bkl 38.90 bkl 43.50 misc ANN misc Glyphosate misc NGS misc Monitoring misc Microbial community composition misc Baltic Sea An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts
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topic_title	550 333.7 DE-600 BIODIV DE-30 fid 38.90 bkl 43.50 bkl An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts ANN Glyphosate NGS Monitoring Microbial community composition Baltic Sea
topic	ddc 550 fid BIODIV bkl 38.90 bkl 43.50 misc ANN misc Glyphosate misc NGS misc Monitoring misc Microbial community composition misc Baltic Sea
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title	An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts
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title_full	An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts
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title_sort	an artificial neural network and random forest identify glyphosate-impacted brackish communities based on 16s rrna amplicon miseq read counts
title_auth	An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	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_str	10.1016/j.marpolbul.2019.110530
up_date	2024-07-06T18:49:42.406Z
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An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

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