Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning.
Comput Struct Biotechnol J
; 19: 2256-2268, 2021.
Article
em En
| MEDLINE
| ID: mdl-33995917
16S rRNA; AMBI, AZTI's marine biotic index; ASV, Amplicon Sequence Variants; AZE, allowable zone of effect, intermediate impact zone; BI, biotic index; BallWa, ballast water dataset; BasCo, Basque coast dataset; Biomonitoring; CE, cage edge; CV, Coefficient of Variance; DADA2, Divisive Amplicon Denoising Algorithm; EQ, environmental quality; Environmental DNA; FM, full model; MDS, multidimensional scaling; Machine learning; Marine; NEB, New England Biolabs; NW, north west; NorSa, Norway salmon dataset; OOB-error, out-of-bag error estimate; PCR, polymerase chain reaction; REF, reference site; RF, random forest algorithm; SML, supervised machine learning; ScoSa, Scottish salmon farm dataset; V3-V4, hypervariable gene regions of the 16s rRNA; bp, base pairs; eDNA, environmental deoxyribonucleic acid; microgAMBI, AZTI's marine biotic index based on microbial genes; mtry, numbers of variables tried at each split; n, number; rRNA, small subunit prokaryotic ribosomal ribonucleic acid
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MEDLINE
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Prognostic_studies
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Risk_factors_studies
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En
Ano de publicação:
2021
Tipo de documento:
Article