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Model selection for machine learning algorithm based on mic..:

Elio L. Herzog (11576876) ; Melania Wäfler (11576879) ; Irene Keller (68341)...
https://figshare.com/articles/figure/Model_selection_for_machine_learning_algorithm_based_on_microbial_abundances_/16827464. , 2021

Link: https://doi.org/10.1371/journal.pone.0258505.s003

RT Journal
T1 <Title>Model selection for machine learning algorithm based on microbial abundances</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16827464&Exemplar=1&LAN=DE
				
A1 Elio L. Herzog (11576876)
A1 Melania Wäfler (11576879)
A1 Irene Keller (68341)
A1 Sebastian Wolf (557132)
A1 Martin S. Zinkernagel (11576882)
A1 Denise C. Zysset-Burri (11174394)
YR 2021
K1 Microbiology
K1 Physiology
K1 Inorganic Chemistry
K1 Science Policy
K1 Environmental Sciences not elsewhere classified
K1 Biological Sciences not elsewhere classified
K1 Chemical Sciences not elsewhere classified
K1 Information Systems not elsewhere classified
K1 microbial balance starts
K1 applying independent algorithms
K1 functional profiles characterized
K1 associated degradation processes
K1 two age groups
K1 matched study groups
K1 later life stages
K1 generate robust data
K1 div >< p
K1 intestinal microbiome plays
K1 intestinal microbiome
K1 functional profiling
K1 study may
K1 stool samples
K1 separated based
K1 select age
K1 second approach
K1 results reflect
K1 preventive strategies
K1 machine learning
K1 linear models
K1 human health
K1 good quality
K1 data analysis
K1 central role
K1 biomarkers connecting
K1 below65 </
K1 associated diseases
K1 above65 </
K1 >
K1 could
K1 278 participants
JF https://figshare.com/articles/figure/Model_selection_for_machine_learning_algorithm_based_on_microbial_abundances_/16827464
LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0258505.s003
DO https://doi.org/10.1371/journal.pone.0258505.s003
SF ELIB - SuUB Bremen

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