Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry

Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry

A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxono...

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Bibliographic Details
Main Authors: Peter Rubbens, Marian L. Schmidt, Ruben Props, Bopaiah A. Biddanda, Nico Boon, Willem Waegeman, Vincent J. Denef
Format: Article
Language:English
Published: American Society for Microbiology 2019-09-01
Series:mSystems
Subjects:
16s rrna
aquatic microbiology
bacterioplankton
flow cytometry
heterotrophic productivity
machine learning
variable selection
Online Access:https://doi.org/10.1128/mSystems.00093-19

Internet

https://doi.org/10.1128/mSystems.00093-19

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