换一批Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning.
第一作者：
Verena,Dully
第一单位：
Technische Universität Kaiserslautern, Ecology, D-67663 Kaiserslautern, Germany.
作者：
关键词
16S rRNAAMBI, AZTI's marine biotic indexASV, Amplicon Sequence VariantsAZE, allowable zone of effect, intermediate impact zoneBI, biotic indexBallWa, ballast water datasetBasCo, Basque coast datasetBiomonitoringCE, cage edgeCV, Coefficient of VarianceDADA2, Divisive Amplicon Denoising AlgorithmEQ, environmental qualityEnvironmental DNAFM, full modelMDS, multidimensional scalingMachine learningMarineNEB, New England BiolabsNW, north westNorSa, Norway salmon datasetOOB-error, out-of-bag error estimatePCR, polymerase chain reactionREF, reference siteRF, random forest algorithmSML, supervised machine learningScoSa, Scottish salmon farm datasetV3-V4, hypervariable gene regions of the 16s rRNAbp, base pairseDNA, environmental deoxyribonucleic acidmicrogAMBI, AZTI's marine biotic index based on microbial genesmtry, numbers of variables tried at each splitn, numberrRNA, small subunit prokaryotic ribosomal ribonucleic acid
DOI
10.1016/j.csbj.2021.04.005
PMID
33995917
发布时间
2021-05-18
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